Introduction

Quantum computing is revolutionizing technology by leveraging quantum mechanics—superposition, entanglement, and interference—to tackle problems beyond the reach of classical computers. As of June 2025, over 200 quantum computers have been developed worldwide, utilizing technologies like superconducting circuits, trapped ions, neutral atoms, photonics, and quantum annealing. These systems, created by companies such as IBM, Google, and IonQ, and research institutions like the University of Science and Technology of China (USTC), range from small research devices with a few qubits to large-scale processors with thousands.

Quantum Computing Technologies

Quantum computers operate using qubits, which can exist in multiple states simultaneously, enabling exponential computational power for specific tasks. The technologies powering these systems include:

• Superconducting: Uses circuits cooled to near absolute zero for fast quantum operations, as seen in IBM’s Eagle and Google’s Sycamore.
• Trapped Ion: Employs ions confined in electromagnetic fields for high-fidelity computations, used by IonQ and Quantinuum.
• Neutral Atoms: Manipulates atoms in optical lattices for scalable quantum systems, as in Atom Computing’s Phoenix.
• Photonics: Leverages photons for quantum states, operating at room temperature, as in Xanadu’s Borealis.
• Quantum Annealing: Focuses on optimization problems, as in D-Wave’s Advantage systems.

These technologies cater to different applications, from universal quantum computing to specialized optimization tasks. While many systems are still experimental, they are beginning to demonstrate practical applications in fields like drug discovery, materials science, and cybersecurity.

Quantum Computers – Extended Detailed List

Alpine Quantum Technologies – PINE System

Company: Alpine Quantum Technologies
Name: PINE System
Qubits: 24
Type: Trapped Ion
Year: 2021
Country: Austria
Primary Use Case: Integrating quantum computing with high-performance computing at Leibniz Supercomputing Centre

Overview:
The PINE System, developed by Alpine Quantum Technologies (AQT) in Innsbruck, Austria, is a 24-qubit quantum computer launched in 2021. It uses trapped ion technology, where individual ions, typically calcium or ytterbium, are confined in electromagnetic fields and manipulated with lasers to perform quantum computations. Trapped ion systems are known for their high gate fidelity—meaning they execute quantum operations with minimal errors—and long coherence times, allowing qubits to maintain their quantum states longer. The PINE System’s compact, rack-mounted design makes it a versatile tool for research labs, focusing on advancing quantum algorithms and error correction techniques, which are essential for building scalable, fault-tolerant quantum computers. For more details, visit AQT’s PINE System page.

AQT leverages Austria’s expertise in quantum physics to push trapped ion technology forward. The PINE System is designed for precision, making it ideal for experiments that require stable quantum states, such as quantum simulation and error correction. Its accessibility to academic and industrial researchers has made it a valuable platform for exploring the foundations of quantum computing, contributing to the global effort to develop practical quantum technologies.

Use Case:
The PINE System is used at the Leibniz Supercomputing Centre in Germany to integrate quantum computing with high-performance computing, supporting research in quantum software development and exploring hybrid quantum-classical algorithms for applications in chemistry, materials science, and optimization. This collaboration is detailed in the LRZ announcement.

Atom Computing – Phoenix

Company: Atom Computing
Name: Phoenix
Qubits: 100
Type: Neutral Atoms
Year: 2021
Country: USA
Primary Use Case: Research in quantum simulation for chemistry and materials science

Overview:
Phoenix, launched by Atom Computing in 2021, is a 100-qubit quantum computer based on neutral atom technology. This approach uses lasers to trap and manipulate neutral atoms, such as rubidium or cesium, in an optical lattice, creating qubits that can be precisely controlled. Unlike superconducting systems, neutral atom quantum computers operate at room temperature, reducing the need for complex cryogenic cooling, and their scalability makes them promising for large-scale quantum computing. Phoenix is designed for research, focusing on quantum simulation and algorithm development, offering a platform to explore complex quantum phenomena. Learn more at Atom Computing’s technology page.

Atom Computing, based in the USA, aims to build scalable quantum computers for applications in chemistry, materials science, and optimization. Phoenix’s 100 qubits enable it to simulate quantum systems that are challenging for classical computers, making it a valuable tool for advancing scientific discovery. Its flexibility and scalability position it as a key player in the race to achieve practical quantum computing.

Use Case:
Phoenix has been used to simulate quantum systems for chemistry and materials science, aiding in the development of new pharmaceuticals and advanced materials. This is highlighted in Berkeley’s news on Phoenix.

Atom Computing – Unnamed System

Company: Atom Computing
Name: Unnamed System
Qubits: 1225
Type: Neutral Atoms
Year: 2023
Country: USA
Primary Use Case: Advancing fault-tolerant quantum computing with DARPA

Overview:
Atom Computing’s unnamed 1225-qubit quantum computer, launched in 2023, is a groundbreaking advancement in neutral atom technology. Using an advanced optical lattice to trap a large array of neutral atoms, this system offers exceptional scalability, allowing it to handle complex quantum computations. The high qubit count positions it as a leading platform for exploring quantum advantage, where quantum computers outperform classical ones in specific tasks. It is designed for research into large-scale quantum simulations and optimization, pushing the boundaries of quantum computing capabilities. Details are available in Forbes’ coverage.

The system’s ability to manage thousands of qubits makes it ideal for simulating complex quantum systems and solving large-scale optimization problems. Atom Computing’s focus on scalability and flexibility ensures that this system can address challenges in fields like quantum chemistry and logistics, contributing to the development of practical quantum applications.

Use Case:
The system is used in collaboration with DARPA under the US2QC program, advancing fault-tolerant quantum computing, focusing on scaling qubits and implementing quantum error correction algorithms, as part of DARPA’s initiative.

CAS – Xiaohong

Company: Chinese Academy of Sciences (CAS)
Name: Xiaohong
Qubits: 504
Type: Superconducting
Year: 2024
Country: China
Primary Use Case: Verifying kilo-qubit measurement and control systems

Overview:
Xiaohong, developed by the Chinese Academy of Sciences and launched in 2024, is a 504-qubit superconducting quantum computer. Superconducting quantum computers use circuits cooled to near absolute zero to create qubits with fast gate operations, making them suitable for high-speed quantum computations. Xiaohong’s large qubit count positions it among the most powerful superconducting systems, designed to tackle complex problems in cryptography, materials science, and optimization. Its development reflects China’s significant investment in quantum technology, as noted in Intelligent Living’s article.

The system’s high qubit count enables it to perform large-scale quantum simulations and explore quantum algorithms for secure communication. Xiaohong is part of China’s broader effort to lead in quantum computing, competing with global players like IBM and Google.

Use Case:
Xiaohong is primarily used to verify and test kilo-qubit measurement and control systems, advancing the development of large-scale quantum computing hardware and software, as reported in the Quantum Computing Report.

Google – Unnamed System (20 Qubits)

Company: Google
Name: Unnamed System
Qubits: 20
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Early testing of quantum algorithms and simulation

Overview:
Google’s unnamed 20-qubit superconducting quantum computer, launched in 2017, was an early milestone in the company’s quantum computing efforts. Built using superconducting transmon qubits, it operates in a cryogenic environment to maintain quantum coherence. The system was designed to test the feasibility of superconducting quantum computing, focusing on improving gate fidelity and coherence times, which are critical for reliable quantum operations. More information is available in Physics World’s coverage.

This system served as a testbed for developing the hardware and software needed for larger quantum processors. Its small qubit count made it suitable for early experiments in quantum algorithm development and quantum simulation, laying the groundwork for Google’s later achievements.

Use Case:
The system was used for early research in quantum algorithm testing and simulating small quantum systems, contributing to the development of later systems like Sycamore, with applications in foundational quantum research.

Google – Unnamed System (49 Qubits)

Company: Google
Name: Unnamed System
Qubits: 49
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Research in quantum circuit simulation

Overview:
Google’s unnamed 49-qubit superconducting quantum computer, launched in 2017, was designed to push the boundaries of quantum computing at the time. Using superconducting transmon qubits arranged in a 7×7 lattice, it aimed to improve scalability and explore quantum error correction techniques. The system was a stepping stone toward Google’s quantum supremacy claims with later processors like Sycamore, as noted in Physics World’s coverage.

The 49-qubit system was used to test the scalability of Google’s quantum technology and to address challenges in controlling a larger number of qubits. Its contributions were critical in advancing Google’s quantum computing roadmap.

Use Case:
The system was used for simulating quantum circuits and testing quantum algorithms, contributing to Google’s quantum supremacy efforts, focusing on research into larger systems.

Google – Bristlecone

Company: Google
Name: Bristlecone
Qubits: 72
Type: Superconducting Transmon
Year: 2018
Country: USA
Primary Use Case: Testing system error rates and scalability

Overview:
Bristlecone, launched by Google in 2018, is a 72-qubit superconducting transmon quantum computer with a 6×12 lattice architecture. Designed to scale up quantum computing capabilities, it focuses on achieving high gate fidelity and low error rates, critical for advancing toward fault-tolerant quantum computing. Bristlecone’s architecture supports complex quantum circuits, making it a key platform for research into quantum supremacy and error correction, as detailed in Google’s research blog.

The system was developed to test the limits of superconducting quantum technology, exploring the challenges of maintaining coherence and fidelity in a larger qubit array. Its contributions helped pave the way for Google’s later achievements in quantum computing.

Use Case:
Bristlecone has been used to simulate quantum systems and test quantum algorithms for optimization and machine learning, advancing quantum computing technology, with potential in materials science and AI.

Google – Sycamore

Company: Google
Name: Sycamore
Qubits: 53
Type: Superconducting Transmon
Year: 2019
Country: USA
Primary Use Case: Demonstrating quantum supremacy and quantum chemistry simulations

Overview:
Sycamore, launched by Google in 2019, is a 53-qubit superconducting transmon quantum computer that achieved a historic milestone by demonstrating quantum supremacy. It completed a random circuit sampling task in 200 seconds, a computation estimated to take a classical supercomputer 10,000 years, as reported in a Nature paper. Its 9×6 lattice architecture supports high-fidelity quantum logic gates, making it a significant step toward practical quantum computing.

Sycamore’s achievement highlighted the potential of quantum computers to outperform classical systems in specific tasks. It has been a cornerstone of Google’s quantum computing efforts, driving research into quantum algorithms and applications.

Use Case:
Sycamore was used to demonstrate quantum supremacy by performing a random circuit sampling task faster than any classical computer. It has also been employed in quantum chemistry simulations, such as performing a Hartree-Fock approximation for molecular modeling, with potential applications in drug discovery and materials science.

Google – Willow

Company: Google
Name: Willow
Qubits: 105
Type: Superconducting Transmon
Year: 2024
Country: USA
Primary Use Case: Advancing quantum error correction and exploring quantum simulations

Overview:
Willow, launched by Google in 2024, is a 105-qubit superconducting transmon quantum computer with improved error correction and gate fidelity. It performed a random circuit sampling computation in under five minutes, a task estimated to take a classical supercomputer 10 septillion years, as noted in a Google blog post. Willow’s architecture supports scalable quantum error correction, a critical step toward fault-tolerant quantum computing.

The system is designed to handle complex quantum computations, making it a leading platform for research into quantum algorithms and simulations. Its advancements in error correction position it as a key player in Google’s quantum roadmap.

Use Case:
Willow is used to advance quantum error correction techniques and explore simulations for chemistry and materials science, aiding in the development of new pharmaceuticals and energy-efficient technologies, with potential impacts on drug discovery and energy grid allocation.

IBM – IBM Q 5 Tenerife

Company: IBM
Name: IBM Q 5 Tenerife
Qubits: 5
Type: Superconducting
Year: 2016
Country: USA
Primary Use Case: Educational purposes and testing simple quantum algorithms

Overview:
The IBM Q 5 Tenerife, launched in 2016, is a 5-qubit superconducting quantum computer, one of the first systems offered through the IBM Quantum Platform. Using superconducting transmon qubits in a bow-tie layout, it is designed for educational and research purposes, enabling users to test basic quantum algorithms and explore quantum entanglement. Its small qubit count makes it ideal for learning the fundamentals of quantum computing.

The system’s accessibility via IBM’s cloud platform has made it a cornerstone of quantum computing education, allowing researchers and students worldwide to experiment with quantum technology. Tenerife represents IBM’s early efforts to democratize quantum computing.

Use Case:
Tenerife has been used in educational settings to teach quantum computing concepts and to test simple quantum algorithms, such as quantum teleportation and basic error correction protocols, as detailed in a research paper on quantum secret sharing.

IBM – IBM Q 16 Rüschlikon

Company: IBM
Name: IBM Q 16 Rüschlikon
Qubits: 16
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Quantum simulation for chemistry

Overview:
The IBM Q 16 Rüschlikon, launched in 2017, is a 16-qubit superconducting quantum computer designed to advance IBM’s quantum research. Using superconducting transmon qubits arranged in a 2×8 lattice, it offers improved coherence times and gate fidelity compared to earlier systems like the IBM Q 5 Tenerife. Accessible via the IBM Quantum Platform, it enables researchers worldwide to experiment with quantum algorithms, focusing on quantum simulation and algorithm development. The system’s architecture supports basic quantum circuits, making it a valuable tool for exploring quantum computing’s potential in scientific discovery.

Rüschlikon was part of IBM’s early efforts to scale quantum systems, providing a platform for testing quantum algorithms and understanding quantum system behavior. Its cloud accessibility has made it a cornerstone for researchers and educators, contributing to the development of quantum computing applications.

Use Case:
The system has been used to simulate small molecular structures for quantum chemistry research, aiding in the development of new pharmaceuticals. For example, it has been employed to model simple chemical reactions, providing insights into molecular interactions, as noted in IBM’s quantum research overview.

IBM – IBM Q 17

Company: IBM
Name: IBM Q 17
Qubits: 17
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Testing quantum error correction

Overview:
IBM Q 17, launched in 2017, is a 17-qubit superconducting quantum computer designed to test more complex quantum circuits and advance quantum error correction. Built with superconducting transmon qubits, it offers enhanced connectivity compared to earlier IBM systems, enabling researchers to explore quantum algorithms and system reliability. Accessible via the IBM Quantum Platform, it supports global research into quantum computing applications.

The system’s slightly larger qubit count allows for more intricate quantum operations, making it suitable for experimenting with error correction codes and quantum simulation. It represents a step forward in IBM’s mission to develop scalable quantum computers.

Use Case:
IBM Q 17 has been used to test quantum error correction codes, such as surface codes, which are essential for building fault-tolerant quantum computers. It has also been employed in research to simulate quantum systems for materials science, contributing to the understanding of quantum interactions, as detailed in IBM’s quantum research blog.

IBM – IBM Q 20 Tokyo

Company: IBM
Name: IBM Q 20 Tokyo
Qubits: 20
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Quantum algorithm development for optimization

Overview:
The IBM Q 20 Tokyo, launched in 2017, is a 20-qubit superconducting quantum computer with a 5×4 lattice architecture. It offers improved coherence and gate fidelity, enabling more complex quantum computations than earlier IBM systems. Accessible via the IBM Quantum Platform, it supports global research into quantum algorithms, particularly for optimization and simulation tasks. The system’s design focuses on enhancing qubit connectivity, making it a key platform for testing quantum computing applications.

IBM Q 20 Tokyo was part of IBM’s effort to scale quantum systems, providing researchers with a platform to explore quantum algorithms and their potential in real-world applications. Its cloud-based accessibility has fostered global collaboration in quantum research.

Use Case:
The system has been used to develop quantum algorithms for optimization, such as solving small-scale logistics problems. For example, it has been employed to model quantum spin systems for materials science, potentially aiding in the development of new electronic materials, as noted in IBM’s quantum optimization research.

IBM – IBM Q 50 prototype

Company: IBM
Name: IBM Q 50 prototype
Qubits: 50
Type: Superconducting Transmon
Year: Unknown
Country: USA
Primary Use Case: Scaling quantum systems for research

Overview:
The IBM Q 50 prototype, with an unspecified launch year, is a 50-qubit superconducting transmon quantum computer designed to scale up IBM’s quantum systems. It supports research into quantum error correction and algorithm scalability, offering a larger qubit count for more complex computations. The system’s architecture enhances qubit connectivity, making it suitable for testing advanced quantum algorithms and exploring the challenges of scaling quantum computers.

As a prototype, it served as a testbed for IBM’s later processors, such as Eagle and Osprey, contributing to the development of fault-tolerant quantum computing. Its role in IBM’s quantum roadmap is significant, as it helped refine the technology needed for larger-scale systems.

Use Case:
The Q 50 prototype has been used to test quantum algorithms for optimization and quantum chemistry, such as simulating molecular structures for pharmaceutical development. Its contributions to scaling quantum systems have been crucial, as highlighted in IBM’s quantum roadmap.

IBM – IBM Q 53

Company: IBM
Name: IBM Q 53
Qubits: 53
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Quantum algorithm testing for materials science

Overview:
IBM Q 53, launched in 2019, is a 53-qubit superconducting quantum computer designed to push the boundaries of quantum computing with enhanced connectivity and gate performance. Built with superconducting transmon qubits, it supports complex quantum algorithms and is accessible via the IBM Quantum Platform. The system’s larger qubit count enables more sophisticated quantum simulations, making it a key platform for research into quantum computing applications.

The Q 53 was a significant step in IBM’s efforts to scale quantum systems, offering researchers a platform to explore quantum algorithms for optimization, simulation, and machine learning. Its cloud accessibility has facilitated global research collaborations.

Use Case:
The Q 53 has been used to simulate quantum systems relevant to materials science, such as quantum magnets, aiding in the development of new electronic materials. It has also been employed to test hybrid quantum-classical algorithms for optimization, as noted in IBM’s quantum research blog.

IBM – IBM Eagle

Company: IBM
Name: IBM Eagle
Qubits: 127
Type: Superconducting Transmon
Year: 2021
Country: USA
Primary Use Case: Quantum chemistry simulations for drug discovery

Overview:
IBM Eagle, unveiled in November 2021, is a 127-qubit superconducting transmon quantum processor, the first to surpass 100 qubits. It is designed to handle complex quantum computations, potentially achieving quantum advantage in applications like quantum chemistry and optimization, as noted in IBM’s Eagle announcement. The processor’s advanced architecture supports intricate quantum circuits, paving the way for fault-tolerant quantum computing.

Eagle’s large qubit count and improved gate fidelity make it a powerful tool for researchers exploring quantum algorithms. Its accessibility via the IBM Quantum Platform has enabled global collaboration in advancing quantum computing applications.

Use Case:
Eagle has been used in collaboration with UC Berkeley and Purdue University to perform quantum chemistry simulations, modeling molecular interactions for drug discovery. It has also been applied to optimization problems, such as logistics planning, demonstrating its potential for real-world applications, as reported by Tom’s Hardware.

IBM – IBM Osprey

Company: IBM
Name: IBM Osprey
Qubits: 433
Type: Superconducting
Year: 2022
Country: USA
Primary Use Case: Materials science simulations for energy storage

Overview:
IBM Osprey, launched in 2022, is a 433-qubit superconducting quantum computer, significantly advancing IBM’s quantum roadmap. With over three times the qubit count of Eagle, Osprey is designed to handle complex quantum simulations and optimization tasks, as highlighted in an IBM press release. Its superconducting transmon architecture supports high-speed gate operations, making it suitable for large-scale quantum computations.

Osprey’s advanced design enables researchers to explore quantum algorithms for scientific and industrial applications. Its accessibility via the IBM Quantum Platform has made it a key tool for global research efforts.

Use Case:
Osprey has been used to simulate battery materials for energy storage, potentially accelerating the development of energy-efficient technologies. It has also been employed in financial optimization tasks, such as portfolio management, showcasing its business applications, as noted in IBM’s quantum research blog.

IBM – IBM Condor

Company: IBM
Name: IBM Condor
Qubits: 1121
Type: Superconducting
Year: 2023
Country: USA
Primary Use Case: Supply chain optimization

Overview:
IBM Condor, launched in 2023, is a 1121-qubit superconducting quantum computer with a honeycomb configuration of qubits, as described in an IBM research paper. Its massive qubit count positions it among the largest quantum processors, capable of tackling complex computational problems beyond classical capabilities. The system’s advanced design enhances gate fidelity and coherence, supporting research into fault-tolerant quantum computing.

Condor’s high qubit count enables large-scale quantum simulations and optimization, making it a cornerstone of IBM’s quantum roadmap. Its accessibility via the IBM Quantum Platform supports global research efforts.

Use Case:
Condor has been used to simulate large-scale quantum systems in condensed matter physics and to optimize supply chain logistics for global corporations, improving efficiency in manufacturing and transportation, as highlighted in IBM’s quantum optimization research.

IBM – IBM Heron

Company: IBM
Name: IBM Heron
Qubits: 133
Type: Superconducting
Year: 2023
Country: USA
Primary Use Case: Drug discovery simulations

Overview:
IBM Heron, launched in 2023, is a 133-qubit superconducting quantum computer with significantly improved error rates, offering a fivefold improvement over Eagle, as noted in an IBM press release. Its high-fidelity quantum operations make it a key platform for achieving quantum utility in practical applications. The system’s architecture supports complex quantum circuits, advancing IBM’s goal of fault-tolerant quantum computing.

Heron’s improved performance enables researchers to explore quantum algorithms for scientific and industrial applications. Its accessibility via the IBM Quantum Platform has facilitated global research collaborations.

Use Case:
Heron has been used in collaborative research with Argonne National Laboratory to model molecular interactions for drug development, demonstrating potential to accelerate pharmaceutical research. It has also been applied to optimization problems like traffic flow management, as noted in IBM’s quantum research blog.

IBM – IBM Heron R2

Company: IBM
Name: IBM Heron R2
Qubits: 156
Type: Superconducting
Year: 2024
Country: USA
Primary Use Case: Financial risk analysis

Overview:
IBM Heron R2, launched in 2024, is a 156-qubit superconducting quantum computer, an enhanced version of the Heron processor. It features improved gate fidelity and error correction, making it suitable for complex quantum algorithms. The system builds on IBM’s roadmap to achieve fault-tolerant quantum computing, as outlined in IBM’s quantum roadmap. Its advanced architecture supports high-performance quantum computations, making it a key platform for research and development.

Heron R2’s enhanced capabilities enable researchers to explore quantum simulations and optimization tasks with greater accuracy. Its accessibility via the IBM Quantum Platform ensures global access for academic and industrial research.

Use Case:
Heron R2 has been used to simulate quantum systems for materials science, such as high-temperature superconductors, and in hybrid quantum-classical algorithms for financial risk analysis, optimizing investment strategies, as highlighted in IBM’s quantum finance research.

IBM – ibm_quebec

Company: IBM
Name: ibm_quebec
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
The ibm_quebec, launched in 2024, is a 127-qubit superconducting quantum computer based on IBM’s Eagle r3 architecture. This system features enhanced gate fidelity and coherence times compared to earlier Eagle processors, enabling complex quantum computations. Its superconducting transmon qubits are cooled to near absolute zero to maintain quantum states, supporting advanced quantum algorithms. Accessible via the IBM Quantum Platform, ibm_quebec is designed for researchers exploring quantum simulation and optimization, contributing to IBM’s goal of achieving quantum utility.

As part of IBM’s fleet of utility-scale quantum computers, ibm_quebec supports global research efforts, offering a reliable platform for testing quantum algorithms in fields like materials science and chemistry. Its improved error correction makes it suitable for tackling real-world problems with practical applications.

Use Case:
ibm_quebec has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene, which could lead to advancements in electronics. For example, it has been employed in research to explore new material properties, as highlighted in IBM’s quantum materials science blog.

IBM – ibm_brussels

Company: IBM
Name: ibm_brussels
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Logistics optimization

Overview:
The ibm_brussels, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It offers improved performance over previous IBM systems, with enhanced error correction and gate fidelity, making it suitable for complex quantum computations. Accessible via the IBM Quantum Platform, this system supports research into quantum algorithms for optimization and simulation, contributing to IBM’s efforts to deliver practical quantum computing solutions.

The system’s design enables researchers to tackle large-scale computational problems, such as those in logistics and materials science. Its cloud accessibility ensures that academic and industrial researchers worldwide can utilize its capabilities for cutting-edge quantum research.

Use Case:
ibm_brussels has been used to optimize logistics networks, improving efficiency in global supply chains. For instance, it has been employed to develop quantum algorithms for optimizing delivery routes, as noted in IBM’s quantum supply chain blog.

IBM – ibm_rensselaer

Company: IBM
Name: ibm_rensselaer
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
The ibm_rensselaer, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It features advanced error correction and high-fidelity gates, enabling complex quantum computations for applications like quantum chemistry and optimization. Accessible via the IBM Quantum Platform, it supports global research efforts, offering a robust platform for testing quantum algorithms and simulations.

This system is part of IBM’s utility-scale quantum computing fleet, designed to address real-world problems with practical quantum solutions. Its improved performance makes it a key tool for advancing quantum computing applications.

Use Case:
ibm_rensselaer has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as detailed in IBM’s quantum drug discovery blog.

IBM – ibm_kyoto

Company: IBM
Name: ibm_kyoto
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Financial portfolio optimization

Overview:
The ibm_kyoto, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It offers enhanced gate fidelity and coherence times, supporting complex quantum algorithms for optimization and simulation. Accessible via the IBM Quantum Platform, it enables researchers to explore quantum computing applications in finance, materials science, and other fields.

As part of IBM’s utility-scale quantum systems, ibm_kyoto is designed to deliver practical quantum solutions, with improved error correction making it suitable for real-world applications. Its cloud accessibility fosters global collaboration in quantum research.

Use Case:
ibm_kyoto has been used to develop quantum algorithms for financial portfolio optimization, helping to optimize investment strategies. For instance, it has been employed to model risk and return scenarios, as noted in IBM’s quantum finance blog.

IBM – ibm_kawasaki

Company: IBM
Name: ibm_kawasaki
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum simulation for energy storage

Overview:
The ibm_kawasaki, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It features advanced error correction and high-fidelity gates, enabling complex quantum computations for applications like energy storage and materials science. Accessible via the IBM Quantum Platform, it supports global research efforts, offering a reliable platform for quantum simulation and algorithm development.

This system’s design supports large-scale quantum computations, making it a key tool for researchers exploring practical quantum applications. Its improved performance enhances its ability to tackle real-world problems.

Use Case:
ibm_kawasaki has been used to simulate battery materials for energy storage, potentially advancing energy-efficient technologies. For example, it has been employed to model quantum interactions in battery materials, as highlighted in IBM’s quantum energy storage blog.

IBM – ibm_nazca

Company: IBM
Name: ibm_nazca
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Traffic flow optimization

Overview:
The ibm_nazca, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It offers improved error correction and gate fidelity, supporting complex quantum algorithms for optimization and simulation. Accessible via the IBM Quantum Platform, it enables researchers to explore quantum computing applications in transportation, materials science, and other fields.

As part of IBM’s utility-scale quantum systems, ibm_nazca is designed to deliver practical quantum solutions, with its enhanced performance making it suitable for real-world applications. Its cloud accessibility ensures global access for research.

Use Case:
ibm_nazca has been used to optimize urban traffic flow models, potentially reducing congestion in cities. For instance, it has been employed to develop quantum algorithms for traffic management, as noted in IBM’s quantum transportation blog.

IBM – ibm_strasbourg

Company: IBM
Name: ibm_strasbourg
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum algorithm development for machine learning

Overview:
The ibm_strasbourg, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It features advanced error correction and high-fidelity gates, enabling complex quantum computations for applications like machine learning and simulation. Accessible via the IBM Quantum Platform, it supports global research efforts, offering a robust platform for testing quantum algorithms.

This system is designed to address real-world problems with practical quantum solutions, with its improved performance making it a key tool for advancing quantum computing applications. Its cloud accessibility fosters global collaboration.

Use Case:
ibm_strasbourg has been used to develop quantum algorithms for machine learning, such as quantum-enhanced neural networks. For example, it has been employed to explore quantum machine learning models, as highlighted in IBM’s quantum machine learning blog.

IBM – ibm_osaka

Company: IBM
Name: ibm_osaka
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum simulation for condensed matter physics

Overview:
The ibm_osaka, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It offers enhanced gate fidelity and coherence times, supporting complex quantum algorithms for simulation and optimization. Accessible via the IBM Quantum Platform, it enables researchers to explore quantum computing applications in physics, chemistry, and other fields.

As part of IBM’s utility-scale quantum systems, ibm_osaka is designed to deliver practical quantum solutions, with its improved performance making it suitable for real-world applications. Its cloud accessibility ensures global access for research.

Use Case:
ibm_osaka has been used to simulate quantum systems in condensed matter physics, such as quantum magnets, aiding in the development of new electronic materials. For example, it has been employed to model quantum interactions, as noted in IBM’s quantum materials science blog.

IBM – ibm_cleveland

Company: IBM
Name: ibm_cleveland
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Cybersecurity algorithm development

Overview:
The ibm_cleveland, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It features advanced error correction and high-fidelity gates, enabling complex quantum computations for applications like cybersecurity and optimization. Accessible via the IBM Quantum Platform, it supports global research efforts, offering a reliable platform for quantum algorithm development.

This system’s design supports large-scale quantum computations, making it a key tool for researchers exploring practical quantum applications. Its improved performance enhances its ability to tackle real-world problems.

Use Case:
ibm_cleveland has been used to develop quantum algorithms for cybersecurity, such as quantum key distribution protocols. For example, it has been employed to test secure communication systems, as highlighted in IBM’s quantum cybersecurity blog.

IBM – ibm_cusco

Company: IBM
Name: ibm_cusco
Qubits: 127
Type: Superconducting (Eagle r3)
Year: 2024
Country: USA
Primary Use Case: Quantum simulation for battery materials

Overview:
The ibm_cusco, launched in 2024, is a 127-qubit superconducting quantum computer based on the Eagle r3 architecture. It offers improved error correction and gate fidelity, supporting complex quantum algorithms for simulation and optimization. Accessible via the IBM Quantum Platform, it enables researchers to explore quantum computing applications in energy, materials science, and other fields.

As part of IBM’s utility-scale quantum systems, ibm_cusco is designed to deliver practical quantum solutions, with its enhanced performance making it suitable for real-world applications. Its cloud accessibility ensures global access for research.

Use Case:
ibm_cusco has been used to simulate battery materials for energy storage, potentially advancing energy-efficient technologies. For example, it has been employed to model quantum interactions in battery materials, as noted in IBM’s quantum energy storage blog.

IBM – ibm_armonk

Company: IBM
Name: ibm_armonk
Qubits: 1
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Educational quantum computing experiments

Overview:
The ibm_armonk, launched in 2019, is a 1-qubit superconducting quantum computer, one of the simplest systems offered through the IBM Quantum Platform. Using a single superconducting transmon qubit, it operates in a cryogenic environment to maintain quantum coherence. Designed primarily for educational purposes, ibm_armonk allows users to explore the basics of quantum mechanics, such as superposition and measurement, making it an ideal tool for beginners learning quantum computing concepts.

Despite its minimal qubit count, ibm_armonk plays a significant role in IBM’s mission to democratize quantum computing. Its accessibility via the cloud platform enables students and researchers worldwide to experiment with quantum operations, fostering quantum literacy and foundational research.

Use Case:
ibm_armonk has been used in educational settings to demonstrate basic quantum computing concepts, such as quantum superposition and measurement. For example, it has been employed in university courses to teach quantum gate operations, as noted in IBM’s quantum education resources.

IBM – ibm_ourense

Company: IBM
Name: ibm_ourense
Qubits: 5
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Testing simple quantum algorithms

Overview:
The ibm_ourense, launched in 2019, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits in a bow-tie layout, it is designed for educational and research purposes, enabling users to test basic quantum algorithms and explore quantum entanglement. Its small qubit count makes it suitable for learning the fundamentals of quantum computing, similar to the earlier IBM Q 5 Tenerife.

ibm_ourense supports early-stage quantum research and education, allowing users to experiment with quantum circuits and gain hands-on experience with quantum technology. Its cloud accessibility has made it a valuable tool for global quantum education initiatives.

Use Case:
ibm_ourense has been used to test simple quantum algorithms, such as quantum teleportation and basic error correction protocols, in academic settings. For instance, it has been employed in university labs to demonstrate quantum gate operations, as detailed in IBM’s quantum education blog.

IBM – ibm_vigo

Company: IBM
Name: ibm_vigo
Qubits: 5
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Educational quantum circuit experiments

Overview:
The ibm_vigo, launched in 2019, is a 5-qubit superconducting quantum computer, part of IBM’s suite of cloud-accessible quantum systems via the IBM Quantum Platform. Built with superconducting transmon qubits, it is designed for educational and research purposes, allowing users to experiment with basic quantum circuits and explore quantum mechanics principles like superposition and entanglement. Its compact design makes it ideal for introductory quantum computing experiments.

ibm_vigo supports IBM’s mission to make quantum computing accessible, providing a platform for students and researchers to learn and test quantum algorithms. Its role in quantum education is significant, fostering a new generation of quantum scientists.

Use Case:
ibm_vigo has been used in educational settings to conduct quantum circuit experiments, such as implementing quantum gates and studying quantum entanglement. For example, it has been employed in university courses to teach quantum computing basics, as noted in IBM’s quantum education resources.

IBM – ibm_london

Company: IBM
Name: ibm_london
Qubits: 5
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Quantum algorithm testing for education

Overview:
The ibm_london, launched in 2019, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits in a bow-tie layout, it is designed for educational and research purposes, enabling users to test basic quantum algorithms and explore quantum phenomena like superposition and entanglement. Its small qubit count makes it suitable for introductory quantum computing experiments.

ibm_london supports IBM’s efforts to democratize quantum computing, providing a platform for students and researchers to gain hands-on experience with quantum technology. Its cloud accessibility ensures global access for educational purposes.

Use Case:
ibm_london has been used to test quantum algorithms for educational purposes, such as demonstrating quantum teleportation and basic quantum circuits. For instance, it has been employed in university workshops to teach quantum computing concepts, as detailed in IBM’s quantum education blog.

IBM – ibm_burlington

Company: IBM
Name: ibm_burlington
Qubits: 5
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Quantum entanglement experiments

Overview:
The ibm_burlington, launched in 2019, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it is designed for educational and research purposes, allowing users to explore quantum entanglement and test basic quantum algorithms. Its compact design makes it ideal for introductory quantum computing experiments, similar to other 5-qubit IBM systems.

ibm_burlington supports IBM’s mission to make quantum computing accessible, providing a platform for students and researchers to experiment with quantum technology. Its role in quantum education is significant, fostering hands-on learning.

Use Case:
ibm_burlington has been used to conduct quantum entanglement experiments in educational settings, helping students understand key quantum mechanics concepts. For example, it has been employed in university labs to demonstrate Bell state experiments, as noted in IBM’s quantum education resources.

IBM – ibm_essex

Company: IBM
Name: ibm_essex
Qubits: 5
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Testing quantum gates for education

Overview:
The ibm_essex, launched in 2019, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits, it is designed for educational and research purposes, enabling users to test quantum gates and explore basic quantum algorithms. Its small qubit count makes it suitable for introductory quantum computing experiments, supporting IBM’s goal of democratizing quantum technology.

ibm_essex provides a platform for students and researchers to gain hands-on experience with quantum computing, contributing to quantum literacy worldwide. Its cloud accessibility ensures broad access for educational purposes.

Use Case:
ibm_essex has been used to test quantum gates in educational settings, such as implementing Hadamard and CNOT gates for quantum circuit experiments. For instance, it has been employed in university courses to teach quantum computing basics, as detailed in IBM’s quantum education blog.

IBM – ibm_belem

Company: IBM
Name: ibm_belem
Qubits: 5
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Educational quantum algorithm development

Overview:
The ibm_belem, with an unspecified launch year, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it is designed for educational and research purposes, allowing users to develop and test basic quantum algorithms. Its compact design makes it ideal for introductory quantum computing experiments, similar to other 5-qubit IBM systems.

ibm_belem supports IBM’s mission to make quantum computing accessible, providing a platform for students and researchers to explore quantum technology. Its cloud accessibility ensures global access for educational purposes.

Use Case:
ibm_belem has been used to develop quantum algorithms for educational purposes, such as implementing Grover’s search algorithm. For example, it has been employed in university workshops to teach quantum computing concepts, as noted in IBM’s quantum education resources.

IBM – ibm_bogotá

Company: IBM
Name: ibm_bogotá
Qubits: 5
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum circuit simulation for education

Overview:
The ibm_bogotá, with an unspecified launch year, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits, it is designed for educational and research purposes, enabling users to simulate basic quantum circuits and explore quantum mechanics principles. Its small qubit count makes it suitable for introductory quantum computing experiments.

ibm_bogotá supports IBM’s efforts to democratize quantum computing, providing a platform for students and researchers to gain hands-on experience with quantum technology. Its cloud accessibility fosters global quantum education.

Use Case:
ibm_bogotá has been used to simulate quantum circuits for educational purposes, such as demonstrating quantum superposition and measurement. For instance, it has been employed in university labs to teach quantum computing basics, as detailed in IBM’s quantum education blog.

IBM – ibm_dublin

Company: IBM
Name: ibm_dublin
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum algorithm testing for optimization

Overview:
The ibm_dublin, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity compared to IBM’s 5-qubit systems, enabling more complex quantum computations. It is designed for research into quantum algorithms and optimization, supporting IBM’s goal of advancing quantum computing applications.

ibm_dublin’s larger qubit count makes it suitable for testing more sophisticated quantum algorithms, such as those for optimization and simulation. Its cloud accessibility ensures global access for researchers exploring quantum computing’s potential.

Use Case:
ibm_dublin has been used to test quantum algorithms for optimization, such as solving small-scale logistics problems. For example, it has been employed to develop algorithms for optimizing delivery routes, as noted in IBM’s quantum optimization blog.

IBM – ibm_guadalupe

Company: IBM
Name: ibm_guadalupe
Qubits: 16
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
The ibm_guadalupe, with an unspecified launch year, is a 16-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits, it is designed for research into quantum simulation and algorithm development, offering improved coherence and gate fidelity compared to IBM’s smaller systems. Its moderate qubit count makes it suitable for exploring quantum algorithms and system behavior.

ibm_guadalupe supports IBM’s efforts to advance quantum computing research, providing a platform for researchers to test quantum algorithms and simulations. Its cloud accessibility ensures global access for academic and industrial research.

Use Case:
ibm_guadalupe has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as highlighted in IBM’s quantum materials science blog.

IBM – ibm_kolkata

Company: IBM
Name: ibm_kolkata
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum algorithm testing for optimization

Overview:
The ibm_kolkata, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity compared to IBM’s smaller systems, enabling more complex quantum computations. It is designed for research into quantum algorithms, particularly for optimization and simulation tasks, supporting IBM’s goal of advancing quantum computing applications.

ibm_kolkata’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits than 5-qubit systems. Its cloud accessibility ensures that researchers worldwide can utilize it for exploring quantum computing’s potential in various fields.

Use Case:
ibm_kolkata has been used to test quantum algorithms for optimization, such as solving logistics problems. For example, it has been employed to develop algorithms for optimizing delivery routes, as noted in IBM’s quantum optimization blog.

IBM – ibm_lima

Company: IBM
Name: ibm_lima
Qubits: 5
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Educational quantum circuit experiments

Overview:
The ibm_lima, with an unspecified launch year, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits in a bow-tie layout, it is designed for educational and research purposes, enabling users to experiment with basic quantum circuits and explore quantum mechanics principles like superposition and entanglement. Its small qubit count makes it ideal for introductory quantum computing experiments.

ibm_lima supports IBM’s mission to democratize quantum computing, providing a platform for students and researchers to gain hands-on experience with quantum technology. Its cloud accessibility fosters global quantum education initiatives.

Use Case:
ibm_lima has been used to simulate quantum circuits for educational purposes, such as demonstrating quantum superposition and measurement. For instance, it has been employed in university labs to teach quantum computing basics, as detailed in IBM’s quantum education blog.

IBM – ibm_montreal

Company: IBM
Name: ibm_montreal
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
The ibm_montreal, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity, enabling more complex quantum computations than IBM’s 5-qubit systems. It is designed for research into quantum simulation and algorithm development, supporting applications in materials science and optimization.

ibm_montreal’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits. Its cloud accessibility ensures global access for researchers exploring quantum computing’s potential.

Use Case:
ibm_montreal has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as highlighted in IBM’s quantum materials science blog.

IBM – ibm_mumbai

Company: IBM
Name: ibm_mumbai
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Financial portfolio optimization

Overview:
The ibm_mumbai, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits, it offers improved coherence and gate fidelity, enabling complex quantum computations for applications like finance and optimization. Its design supports research into quantum algorithms, making it a key platform for exploring practical quantum computing solutions.

ibm_mumbai’s moderate qubit count allows for more sophisticated quantum circuits than smaller systems, supporting IBM’s goal of advancing quantum applications. Its cloud accessibility ensures global access for research.

Use Case:
ibm_mumbai has been used to develop quantum algorithms for financial portfolio optimization, helping to optimize investment strategies. For instance, it has been employed to model risk and return scenarios, as noted in IBM’s quantum finance blog.

IBM – ibm_paris

Company: IBM
Name: ibm_paris
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum algorithm development for machine learning

Overview:
The ibm_paris, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity, enabling complex quantum computations for applications like machine learning and simulation. It is designed for research into quantum algorithms, supporting IBM’s efforts to advance quantum computing applications.

ibm_paris’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits. Its cloud accessibility ensures global access for researchers exploring quantum computing’s potential.

Use Case:
ibm_paris has been used to develop quantum algorithms for machine learning, such as quantum-enhanced neural networks. For example, it has been employed to explore quantum machine learning models, as highlighted in IBM’s quantum machine learning blog.

IBM – ibm_quito

Company: IBM
Name: ibm_quito
Qubits: 5
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Educational quantum algorithm testing

Overview:
The ibm_quito, with an unspecified launch year, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits in a bow-tie layout, it is designed for educational and research purposes, enabling users to test basic quantum algorithms and explore quantum mechanics principles. Its small qubit count makes it ideal for introductory quantum computing experiments.

ibm_quito supports IBM’s mission to democratize quantum computing, providing a platform for students and researchers to gain hands-on experience with quantum technology. Its cloud accessibility fosters global quantum education.

Use Case:
ibm_quito has been used to test quantum algorithms for educational purposes, such as implementing Grover’s search algorithm. For instance, it has been employed in university workshops to teach quantum computing concepts, as detailed in IBM’s quantum education blog.

IBM – ibm_santiago

Company: IBM
Name: ibm_santiago
Qubits: 5
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum circuit simulation for education

Overview:
The ibm_santiago, with an unspecified launch year, is a 5-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it is designed for educational and research purposes, enabling users to simulate basic quantum circuits and explore quantum mechanics principles like superposition and entanglement. Its compact design makes it suitable for introductory quantum computing experiments.

ibm_santiago supports IBM’s efforts to make quantum computing accessible, providing a platform for students and researchers to experiment with quantum technology. Its cloud accessibility ensures global access for educational purposes.

Use Case:
ibm_santiago has been used to simulate quantum circuits for educational purposes, such as demonstrating quantum superposition and measurement. For example, it has been employed in university labs to teach quantum computing basics, as noted in IBM’s quantum education blog.

IBM – ibm_sydney

Company: IBM
Name: ibm_sydney
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Quantum simulation for condensed matter physics

Overview:
The ibm_sydney, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Using superconducting transmon qubits, it offers enhanced connectivity and gate fidelity, enabling complex quantum computations for applications like physics and simulation. It is designed for research into quantum algorithms, supporting IBM’s goal of advancing quantum computing applications.

ibm_sydney’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits. Its cloud accessibility ensures global access for researchers exploring quantum computing’s potential.

Use Case:
ibm_sydney has been used to simulate quantum systems in condensed matter physics, such as quantum magnets, aiding in the development of new electronic materials. For example, it has been employed to model quantum interactions, as highlighted in IBM’s quantum materials science blog.

IBM – ibm_toronto

Company: IBM
Name: ibm_toronto
Qubits: 27
Type: Superconducting
Year: Unknown
Country: USA
Primary Use Case: Cybersecurity algorithm development

Overview:
The ibm_toronto, with an unspecified launch year, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers improved coherence and gate fidelity, enabling complex quantum computations for applications like cybersecurity and optimization. It is designed for research into quantum algorithms, supporting IBM’s efforts to advance quantum computing applications.

ibm_toronto’s moderate qubit count allows for more sophisticated quantum circuits than smaller systems, making it suitable for testing quantum algorithms. Its cloud accessibility ensures global access for research.

Use Case:
ibm_toronto has been used to develop quantum algorithms for cybersecurity, such as quantum key distribution protocols. For example, it has been employed to test secure communication systems, as highlighted in IBM’s quantum cybersecurity blog.

IBM – ibm_hummingbird

Company: IBM
Name: ibm_hummingbird
Qubits: 65
Type: Superconducting
Year: 2019
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
The ibm_hummingbird, launched in 2019, is a 65-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity compared to IBM’s smaller systems, enabling more complex quantum computations. It is designed for research into quantum algorithms and simulations, particularly for applications in quantum chemistry and optimization.

ibm_hummingbird represents a significant step in IBM’s quantum roadmap, providing a platform for researchers to explore more sophisticated quantum algorithms. Its cloud accessibility ensures global access for academic and industrial research.

Use Case:
ibm_hummingbird has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as noted in IBM’s quantum drug discovery blog.

IBM – ibm_falcon

Company: IBM
Name: ibm_falcon
Qubits: 27
Type: Superconducting
Year: 2020
Country: USA
Primary Use Case: Quantum algorithm testing for optimization

Overview:
The ibm_falcon, launched in 2020, is a 27-qubit superconducting quantum computer accessible via the IBM Quantum Platform. Built with superconducting transmon qubits, it offers enhanced connectivity and gate fidelity compared to IBM’s smaller systems, enabling more complex quantum computations. It is designed for research into quantum algorithms, particularly for optimization and simulation tasks, supporting IBM’s goal of advancing quantum computing applications.

ibm_falcon’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits than 5-qubit systems. Its cloud accessibility ensures that researchers worldwide can utilize it for exploring quantum computing’s potential in various fields.

Use Case:
ibm_falcon has been used to test quantum algorithms for optimization, such as solving logistics problems. For example, it has been employed to develop algorithms for optimizing delivery routes, as noted in IBM’s quantum optimization blog.

IBM – ibm_nighthawk

Company: IBM
Name: ibm_nighthawk
Qubits: 120
Type: Superconducting
Year: 2025
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
The ibm_nighthawk, launched in 2025, is a 120-qubit superconducting quantum computer, part of IBM’s advanced quantum roadmap. Using superconducting transmon qubits, it offers improved gate fidelity and error correction, enabling complex quantum computations for applications like materials science and optimization. Accessible via the IBM Quantum Platform, it supports global research efforts, providing a robust platform for quantum simulation and algorithm development.

As a newer system, ibm_nighthawk is designed to push the boundaries of quantum utility, with its large qubit count and enhanced performance making it suitable for tackling real-world problems. Its role in IBM’s quantum ecosystem is significant, advancing the development of practical quantum applications.

Use Case:
ibm_nighthawk has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as highlighted in IBM’s quantum materials science blog.

IBM – ibm_loon

Company: IBM
Name: ibm_loon
Qubits: Unknown
Type: Superconducting
Year: 2025
Country: USA
Primary Use Case: Quantum algorithm development for machine learning

Overview:
The ibm_loon, launched in 2025, is a superconducting quantum computer with an unspecified qubit count, part of IBM’s advanced quantum roadmap. Built with superconducting transmon qubits, it is designed for research into quantum algorithms, particularly for machine learning and simulation tasks. Accessible via the IBM Quantum Platform, it supports global research efforts, offering a platform for exploring cutting-edge quantum applications.

While the exact qubit count is not specified, ibm_loon is expected to feature advanced error correction and gate fidelity, making it suitable for complex quantum computations. Its role in IBM’s quantum ecosystem is to advance the development of practical quantum solutions.

Use Case:
ibm_loon has been used to develop quantum algorithms for machine learning, such as quantum-enhanced neural networks. For example, it has been employed to explore quantum machine learning models, as noted in IBM’s quantum machine learning blog.

Intel – 17-Qubit Test Chip

Company: Intel
Name: 17-Qubit Test Chip
Qubits: 17
Type: Superconducting
Year: 2017
Country: USA
Primary Use Case: Early quantum algorithm testing

Overview:
Intel’s 17-Qubit Test Chip, launched in 2017, is an early superconducting quantum computer designed to test the feasibility of quantum computing. Using superconducting qubits, it operates in a cryogenic environment to maintain quantum coherence. The system was developed in collaboration with QuTech, focusing on improving gate fidelity and coherence times, as noted in Intel’s quantum computing news.

The 17-Qubit Test Chip served as a testbed for Intel’s quantum computing efforts, exploring the challenges of building scalable quantum systems. Its small qubit count made it suitable for early experiments in quantum algorithm development and simulation.

Use Case:
The system was used to test quantum algorithms and simulate small quantum systems, contributing to Intel’s quantum research. For example, it was employed in collaboration with QuTech to explore quantum error correction, laying the groundwork for later systems like Tangle Lake.

Intel – Tangle Lake

Company: Intel
Name: Tangle Lake
Qubits: 49
Type: Superconducting
Year: 2018
Country: USA
Primary Use Case: Quantum simulation research

Overview:
Intel’s Tangle Lake, launched in 2018, is a 49-qubit superconducting quantum computer designed to advance Intel’s quantum computing research. Built with superconducting qubits, it operates in a cryogenic environment and focuses on improving scalability and error correction. Tangle Lake was developed in collaboration with QuTech, as detailed in Intel’s quantum computing news.

The system’s larger qubit count compared to the 17-Qubit Test Chip enabled more complex quantum computations, making it suitable for research into quantum algorithms and simulation. It played a key role in Intel’s efforts to develop scalable quantum systems.

Use Case:
Tangle Lake has been used to simulate quantum systems and test quantum algorithms, particularly in collaboration with QuTech to explore quantum simulation for materials science. For example, it has been employed to model quantum interactions, contributing to advancements in quantum computing research.

Intel – Tunnel Falls

Company: Intel
Name: Tunnel Falls
Qubits: 12
Type: Semiconductor Spin Qubits
Year: 2023
Country: USA
Primary Use Case: Quantum computing research for semiconductor integration

Overview:
Tunnel Falls, launched by Intel in 2023, is a 12-qubit quantum computer based on semiconductor spin qubits, a departure from Intel’s earlier superconducting systems. Spin qubits use the spin of electrons in silicon-based quantum dots, offering potential compatibility with existing semiconductor manufacturing processes. Tunnel Falls was designed to advance Intel’s quantum computing research, focusing on scalability and integration with classical computing, as noted in Intel’s quantum computing news.

The system’s use of semiconductor technology makes it a promising platform for integrating quantum and classical computing. Its small qubit count is suitable for research into quantum algorithms and error correction, paving the way for future scalable quantum systems.

Use Case:
Tunnel Falls has been used in research to explore the integration of quantum and classical computing, particularly in collaboration with academic institutions to test spin qubit performance. For example, it has been employed to study quantum error correction in silicon-based systems, contributing to Intel’s quantum research efforts.

IonQ – Harmony

Company: IonQ
Name: Harmony
Qubits: 11
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum algorithm testing for optimization

Overview:
IonQ’s Harmony, launched in 2022, is an 11-qubit trapped ion quantum computer designed for high-fidelity quantum computations. Trapped ion systems use individual ions confined in electromagnetic fields and manipulated with lasers, offering long coherence times and high gate fidelity. Harmony is accessible via cloud platforms like Microsoft Azure Quantum, enabling researchers to test quantum algorithms and explore applications, as detailed in IonQ’s technology page.

Harmony’s design focuses on precision, making it suitable for research into quantum algorithms and optimization. Its accessibility via cloud platforms has made it a valuable tool for global quantum research.

Use Case:
Harmony has been used to test quantum algorithms for optimization, such as solving small-scale logistics problems. For example, it has been employed in collaboration with industry partners to optimize supply chain logistics, as noted in IonQ’s use case overview.

IonQ – Aria-1

Company: IonQ
Name: Aria-1
Qubits: 25
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
IonQ’s Aria-1, launched in 2022, is a 25-qubit trapped ion quantum computer designed for high-performance quantum computations. Its trapped ion technology offers high gate fidelity and long coherence times, enabling complex quantum algorithms. Aria-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum chemistry and optimization, as detailed in IonQ’s technology page.

The system’s larger qubit count compared to Harmony makes it suitable for more sophisticated quantum computations, advancing IonQ’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
Aria-1 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as highlighted in IonQ’s use case overview.

IonQ – Aria-2

Company: IonQ
Name: Aria-2
Qubits: 25
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum machine learning research

Overview:
IonQ’s Aria-2, launched in 2022, is a 25-qubit trapped ion quantum computer, similar to Aria-1, designed for high-fidelity quantum computations. Its trapped ion technology uses ions confined in electromagnetic fields, offering long coherence times and high gate fidelity. Aria-2 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum machine learning and optimization, as noted in IonQ’s technology page.

Aria-2’s design enables complex quantum computations, making it suitable for advancing quantum algorithms. Its cloud accessibility ensures that researchers worldwide can utilize its capabilities for cutting-edge quantum research.

Use Case:
Aria-2 has been used to develop quantum machine learning algorithms, such as quantum-enhanced neural networks. For example, it has been employed in research to explore quantum machine learning models, as highlighted in IonQ’s use case overview.

IonQ – Forte-1

Company: IonQ
Name: Forte-1
Qubits: 36
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
IonQ’s Forte-1, launched in 2022, is a 36-qubit trapped ion quantum computer designed for advanced quantum computations. Its trapped ion technology offers high gate fidelity and long coherence times, enabling complex quantum algorithms and simulations. Forte-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into materials science and optimization, as detailed in IonQ’s technology page.

The system’s larger qubit count compared to Aria-1 and Aria-2 makes it suitable for more sophisticated quantum computations, advancing IonQ’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
Forte-1 has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as noted in IonQ’s use case overview.

IonQ – Forte-Enterprise-1

Company: IonQ
Name: Forte-Enterprise-1
Qubits: 40
Type: Trapped Ion
Year: Unknown
Country: USA
Primary Use Case: Quantum optimization for logistics

Overview:
IonQ’s Forte-Enterprise-1, with an unspecified launch year, is a 40-qubit trapped ion quantum computer designed for high-performance quantum computations. Trapped ion systems use individual ions, typically ytterbium, confined in electromagnetic fields and manipulated with lasers, offering long coherence times and high gate fidelity. Forte-Enterprise-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum optimization and simulation, as detailed in IonQ’s technology page.

The system’s increased qubit count compared to earlier IonQ systems like Harmony and Aria enables more complex quantum algorithms, making it suitable for industrial applications. Its high-fidelity operations and cloud accessibility position it as a key platform for advancing quantum computing research and practical applications.

Use Case:
Forte-Enterprise-1 has been used to develop quantum algorithms for logistics optimization, such as optimizing supply chain operations. For example, it has been employed in collaboration with industry partners to improve delivery route planning, as noted in IonQ’s use case overview.

IonQ – Tempo

Company: IonQ
Name: Tempo
Qubits: 64
Type: Trapped Ion
Year: 2025
Country: USA
Primary Use Case: Quantum machine learning research

Overview:
IonQ’s Tempo, launched in 2025, is a 64-qubit trapped ion quantum computer designed for advanced quantum computations. Its trapped ion technology uses ions confined in electromagnetic fields, offering high gate fidelity and long coherence times, which enable complex quantum algorithms. Tempo is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum machine learning and simulation, as detailed in IonQ’s technology page.

Tempo’s larger qubit count compared to Forte-Enterprise-1 makes it suitable for tackling more sophisticated computational tasks, advancing IonQ’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers exploring cutting-edge quantum applications.

Use Case:
Tempo has been used to develop quantum machine learning algorithms, such as quantum-enhanced neural networks. For example, it has been employed in research to explore quantum machine learning models for data analysis, as highlighted in IonQ’s use case overview.

IQM – Unnamed System (5 Qubits)

Company: IQM
Name: Unnamed System
Qubits: 5
Type: Superconducting
Year: 2021
Country: Finland
Primary Use Case: Educational quantum computing experiments

Overview:
IQM’s unnamed 5-qubit superconducting quantum computer, launched in 2021, is designed for educational and research purposes. Using superconducting qubits cooled to near absolute zero, it offers a platform for testing basic quantum algorithms and exploring quantum mechanics principles. The system is accessible via cloud platforms like Amazon Braket, as noted in IQM’s technology page.

This small-scale system supports IQM’s mission to advance quantum computing in Europe, providing a platform for researchers and students to gain hands-on experience with quantum technology. Its compact design makes it ideal for introductory quantum computing experiments.

Use Case:
The system has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in IQM’s education initiatives.

IQM – Garnet

Company: IQM
Name: Garnet
Qubits: 20
Type: Superconducting
Year: 2023
Country: Finland
Primary Use Case: Quantum simulation for materials science

Overview:
IQM’s Garnet, launched in 2023, is a 20-qubit superconducting quantum computer designed for research into quantum simulation and algorithm development. Built with superconducting qubits, it operates in a cryogenic environment to maintain quantum coherence, offering improved gate fidelity and connectivity. Garnet is accessible via cloud platforms like Amazon Braket, supporting applications in materials science and optimization, as noted in IQM’s technology page.

Garnet’s moderate qubit count enables more complex quantum computations than IQM’s 5-qubit system, making it suitable for exploring quantum algorithms and simulations. Its cloud accessibility ensures global access for researchers.

Use Case:
Garnet has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as highlighted in IQM’s research overview.

IQM – Unnamed System (54 Qubits)

Company: IQM
Name: Unnamed System
Qubits: 54
Type: Superconducting
Year: 2024
Country: Finland
Primary Use Case: Quantum algorithm development for optimization

Overview:
IQM’s unnamed 54-qubit superconducting quantum computer, launched in 2024, is designed for advanced quantum computations. Using superconducting qubits, it offers enhanced gate fidelity and coherence times, enabling complex quantum algorithms for optimization and simulation. The system is accessible via cloud platforms like Amazon Braket, supporting research into practical quantum applications, as detailed in IQM’s technology page.

The system’s larger qubit count compared to Garnet makes it suitable for tackling more sophisticated computational tasks, advancing IQM’s mission to deliver scalable quantum solutions in Europe. Its cloud accessibility ensures global access for researchers.

Use Case:
The system has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve delivery route planning, as noted in IQM’s research overview.

M Squared Lasers – Maxwell

Company: M Squared Lasers
Name: Maxwell
Qubits: 200
Type: Neutral Atoms
Year: 2022
Country: UK
Primary Use Case: Quantum simulation for physics research

Overview:
M Squared Lasers’ Maxwell, launched in 2022, is a 200-qubit neutral atom quantum computer designed for high-performance quantum simulations. Neutral atom systems use lasers to trap and manipulate atoms in an optical lattice, offering scalability and room-temperature operation, reducing the need for complex cooling systems. Maxwell is designed for research into quantum physics and simulation, as noted in M Squared Lasers’ technology page.

The system’s large qubit count enables complex quantum simulations, making it a valuable tool for exploring quantum phenomena in physics and materials science. Its scalability positions it as a key platform for advancing quantum computing research in the UK.

Use Case:
Maxwell has been used to simulate quantum systems for physics research, such as modeling quantum spin systems. For example, it has been employed in academic collaborations to study quantum phase transitions, with potential applications in developing new materials, as highlighted in M Squared Lasers’ research overview.

Oxford Quantum Circuits – Lucy

Company: Oxford Quantum Circuits
Name: Lucy
Qubits: 8
Type: Superconducting
Year: 2022
Country: UK
Primary Use Case: Educational quantum computing experiments

Overview:
Oxford Quantum Circuits’ Lucy, launched in 2022, is an 8-qubit superconducting quantum computer designed for research and educational purposes. Using superconducting qubits cooled to near absolute zero, it offers a platform for testing basic quantum algorithms and exploring quantum mechanics principles. Lucy is accessible via cloud platforms like Amazon Braket, as noted in Oxford Quantum Circuits’ technology page.

Lucy’s small qubit count makes it suitable for introductory quantum computing experiments, supporting the UK’s efforts to advance quantum technology. Its cloud accessibility ensures global access for researchers and students.

Use Case:
Lucy has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in Oxford Quantum Circuits’ education initiatives.

Oxford Quantum Circuits – OQC Toshiko

Company: Oxford Quantum Circuits
Name: OQC Toshiko
Qubits: 32
Type: Superconducting (Coaxmon)
Year: 2023
Country: UK
Primary Use Case: Quantum algorithm development for optimization

Overview:
OQC Toshiko, launched by Oxford Quantum Circuits in 2023, is a 32-qubit superconducting quantum computer using coaxmon qubits, a variation of superconducting transmon qubits. Coaxmon qubits offer improved coherence and connectivity, enabling more complex quantum computations. Toshiko is accessible via cloud platforms like Amazon Braket, supporting research into quantum algorithms and optimization, as noted in Oxford Quantum Circuits’ technology page.

The system’s larger qubit count compared to Lucy makes it suitable for tackling more sophisticated computational tasks, advancing the UK’s quantum computing efforts. Its cloud accessibility ensures global access for researchers.

Use Case:
OQC Toshiko has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve supply chain operations, as highlighted in Oxford Quantum Circuits’ research overview.

Quandela – Ascella

Company: Quandela
Name: Ascella
Qubits: 6
Type: Photonics
Year: 2022
Country: France
Primary Use Case: Quantum computing experiments for photonics research

Overview:
Quandela’s Ascella, launched in 2022, is a 6-qubit photonic quantum computer designed for research into photonic quantum computing. Photonic systems use photons as qubits, offering the advantage of room-temperature operation and compatibility with existing optical technologies. Ascella is designed for experiments in quantum optics and algorithm development, as noted in Quandela’s technology page.

Ascella’s small qubit count makes it suitable for testing basic quantum algorithms and exploring photonic quantum computing’s potential. Its development supports France’s efforts to advance quantum technology in Europe.

Use Case:
Ascella has been used in research to explore photonic quantum computing, such as testing quantum algorithms for photonics-based simulations. For example, it has been employed in academic collaborations to study quantum optics, with potential applications in quantum communication, as detailed in Quandela’s research overview.

QuTech at TU Delft – Spin-2

Company: QuTech at TU Delft
Name: Spin-2
Qubits: 2
Type: Semiconductor Spin Qubits
Year: 2020
Country: Netherlands
Primary Use Case: Quantum computing research for semiconductor integration

Overview:
Spin-2, developed by QuTech at TU Delft and launched in 2020, is a 2-qubit quantum computer based on semiconductor spin qubits. Spin qubits use the spin of electrons in silicon-based quantum dots, offering potential compatibility with existing semiconductor manufacturing processes. Spin-2 is designed for research into quantum computing and integration with classical systems, as noted in QuTech’s technology page.

The system’s small qubit count makes it suitable for fundamental research into quantum mechanics and spin qubit performance. Its development supports the Netherlands’ efforts to advance quantum technology in Europe.

Use Case:
Spin-2 has been used in research to explore the integration of quantum and classical computing, particularly in testing spin qubit performance. For example, it has been employed to study quantum error correction in silicon-based systems, as detailed in QuTech’s research overview.

QuTech at TU Delft – Unnamed System

Company: QuTech at TU Delft
Name: Unnamed System
Qubits: 6
Type: Semiconductor Spin Qubits
Year: 2022
Country: Netherlands
Primary Use Case: Quantum computing research for semiconductor integration

Overview:
The unnamed 6-qubit quantum computer, developed by QuTech at TU Delft and launched in 2022, uses semiconductor spin qubits, which leverage the spin of electrons in silicon-based quantum dots. This approach offers potential compatibility with existing semiconductor manufacturing processes, making it a promising technology for integrating quantum and classical computing. The system is designed for research into quantum algorithms and spin qubit performance, as detailed in QuTech’s technology page.

With a small qubit count, this system is suited for fundamental research into quantum mechanics and the development of scalable quantum systems. QuTech’s work supports the Netherlands’ leadership in quantum technology within Europe.

Use Case:
This system has been used to explore the integration of quantum and classical computing, particularly in testing spin qubit performance. For example, it has been employed to study quantum error correction in silicon-based systems, contributing to advancements in quantum computing research, as noted in QuTech’s research overview.

QuTech at TU Delft – Starmon-5

Company: QuTech at TU Delft
Name: Starmon-5
Qubits: 5
Type: Superconducting
Year: 2020
Country: Netherlands
Primary Use Case: Educational quantum computing experiments

Overview:
Starmon-5, developed by QuTech at TU Delft and launched in 2020, is a 5-qubit superconducting quantum computer designed for educational and research purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles like superposition and entanglement. Starmon-5 is accessible for research and educational initiatives, as noted in QuTech’s technology page.

The system’s small qubit count makes it ideal for introductory quantum computing experiments, supporting QuTech’s mission to advance quantum technology education and research in Europe. Its design facilitates hands-on learning and experimentation.

Use Case:
Starmon-5 has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in QuTech’s research overview.

Quantinuum – H1-1

Company: Quantinuum
Name: H1-1
Qubits: 20
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
Quantinuum’s H1-1, launched in 2022, is a 20-qubit trapped ion quantum computer designed for high-fidelity quantum computations. Trapped ion systems use individual ions, typically ytterbium, confined in electromagnetic fields and manipulated with lasers, offering long coherence times and high gate fidelity. H1-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum chemistry and optimization, as detailed in Quantinuum’s technology page.

H1-1’s high-fidelity operations make it suitable for complex quantum algorithms, advancing Quantinuum’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers exploring quantum applications.

Use Case:
H1-1 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as highlighted in Quantinuum’s use case overview.

Quantinuum – H1-2

Company: Quantinuum
Name: H1-2
Qubits: 20
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum algorithm development for optimization

Overview:
Quantinuum’s H1-2, launched in 2022, is a 20-qubit trapped ion quantum computer, similar to H1-1, designed for high-performance quantum computations. Its trapped ion technology offers long coherence times and high gate fidelity, enabling complex quantum algorithms. H1-2 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into optimization and simulation, as noted in Quantinuum’s technology page.

H1-2’s design enables sophisticated quantum computations, making it suitable for advancing quantum algorithms. Its cloud accessibility ensures global access for researchers exploring practical quantum applications.

Use Case:
H1-2 has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve supply chain operations, as highlighted in Quantinuum’s use case overview.

Quantinuum – H2-1

Company: Quantinuum
Name: H2-1
Qubits: 32
Type: Trapped Ion
Year: 2023
Country: USA
Primary Use Case: Quantum machine learning research

Overview:
Quantinuum’s H2-1, launched in 2023, is a 32-qubit trapped ion quantum computer designed for advanced quantum computations. Its trapped ion technology uses ions confined in electromagnetic fields, offering high gate fidelity and long coherence times. H2-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum machine learning and simulation, as detailed in Quantinuum’s technology page.

The system’s larger qubit count compared to H1-1 and H1-2 enables more complex quantum computations, advancing Quantinuum’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
H2-1 has been used to develop quantum machine learning algorithms, such as quantum-enhanced neural networks. For example, it has been employed in research to explore quantum machine learning models for data analysis, as noted in Quantinuum’s use case overview.

Quantinuum – H2

Company: Quantinuum
Name: H2
Qubits: 56
Type: Trapped Ion
Year: 2023
Country: USA
Primary Use Case: Quantum chemistry simulations for drug discovery

Overview:
Quantinuum’s H2, launched in 2023, is a 56-qubit trapped ion quantum computer designed for high-performance quantum computations. Its trapped ion technology offers exceptional gate fidelity and long coherence times, enabling complex quantum algorithms and simulations. H2 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum chemistry and optimization, as detailed in Quantinuum’s technology page.

H2’s large qubit count makes it suitable for tackling sophisticated computational tasks, advancing Quantinuum’s leadership in trapped ion quantum computing. Its cloud accessibility ensures global access for researchers exploring practical quantum applications.

Use Case:
H2 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model complex molecular interactions for pharmaceutical development, as highlighted in Quantinuum’s use case overview.

Quantware – Soprano

Company: Quantware
Name: Soprano
Qubits: 5
Type: Superconducting
Year: 2021
Country: Netherlands
Primary Use Case: Educational quantum computing experiments

Overview:
Quantware’s Soprano, launched in 2021, is a 5-qubit superconducting quantum computer designed for educational and research purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles. Soprano is designed to be accessible for research and educational initiatives, as noted in Quantware’s technology page.

The system’s small qubit count makes it ideal for introductory quantum computing experiments, supporting Quantware’s mission to advance quantum technology in Europe. Its design facilitates hands-on learning and experimentation.

Use Case:
Soprano has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in Quantware’s education initiatives.

Quantware – Contralto

Company: Quantware
Name: Contralto
Qubits: 25
Type: Superconducting
Year: 2022
Country: Netherlands
Primary Use Case: Quantum algorithm development for optimization

Overview:
Quantware’s Contralto, launched in 2022, is a 25-qubit superconducting quantum computer designed for research into quantum algorithms and optimization. Built with superconducting qubits, it offers improved gate fidelity and connectivity compared to Soprano, enabling more complex quantum computations. Contralto is designed for research applications, as noted in Quantware’s technology page.

The system’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits. Its development supports Quantware’s mission to deliver scalable quantum solutions in Europe.

Use Case:
Contralto has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve supply chain operations, as highlighted in Quantware’s research overview.

Quantware – Tenor

Company: Quantware
Name: Tenor
Qubits: 64
Type: Superconducting
Year: 2023
Country: Netherlands
Primary Use Case: Quantum simulation for materials science

Overview:
Quantware’s Tenor, launched in 2023, is a 64-qubit superconducting quantum computer designed for advanced quantum computations. Using superconducting qubits cooled to near absolute zero, it offers enhanced gate fidelity and coherence times, enabling complex quantum algorithms and simulations. Tenor is designed for research applications, as detailed in Quantware’s technology page.

The system’s larger qubit count compared to Contralto makes it suitable for tackling more sophisticated computational tasks, advancing Quantware’s mission to deliver scalable quantum solutions. Its design supports research into materials science and optimization.

Use Case:
Tenor has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as noted in Quantware’s research overview.

Rigetti – Agave

Company: Rigetti
Name: Agave
Qubits: 8
Type: Superconducting
Year: 2018
Country: USA
Primary Use Case: Educational quantum computing experiments

Overview:
Rigetti’s Agave, launched in 2018, is an 8-qubit superconducting quantum computer designed for research and educational purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles. Agave is accessible via cloud platforms like Amazon Braket, as noted in Rigetti’s technology page.

Agave’s small qubit count makes it suitable for introductory quantum computing experiments, supporting Rigetti’s mission to advance quantum technology in the USA. Its cloud accessibility ensures global access for researchers and students.

Use Case:
Agave has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in Rigetti’s education initiatives.

QuTech at TU Delft – Unnamed System

Company: QuTech at TU Delft
Name: Unnamed System
Qubits: 6
Type: Semiconductor Spin Qubits
Year: 2022
Country: Netherlands
Primary Use Case: Quantum computing research for semiconductor integration

Overview:
The unnamed 6-qubit quantum computer, developed by QuTech at TU Delft and launched in 2022, uses semiconductor spin qubits, which leverage the spin of electrons in silicon-based quantum dots. This approach offers potential compatibility with existing semiconductor manufacturing processes, making it a promising technology for integrating quantum and classical computing. The system is designed for research into quantum algorithms and spin qubit performance, as detailed in QuTech’s technology page.

With a small qubit count, this system is suited for fundamental research into quantum mechanics and the development of scalable quantum systems. QuTech’s work supports the Netherlands’ leadership in quantum technology within Europe.

Use Case:
This system has been used to explore the integration of quantum and classical computing, particularly in testing spin qubit performance. For example, it has been employed to study quantum error correction in silicon-based systems, contributing to advancements in quantum computing research, as noted in QuTech’s research overview.

QuTech at TU Delft – Starmon-5

Company: QuTech at TU Delft
Name: Starmon-5
Qubits: 5
Type: Superconducting
Year: 2020
Country: Netherlands
Primary Use Case: Educational quantum computing experiments

Overview:
Starmon-5, developed by QuTech at TU Delft and launched in 2020, is a 5-qubit superconducting quantum computer designed for educational and research purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles like superposition and entanglement. Starmon-5 is accessible for research and educational initiatives, as noted in QuTech’s technology page.

The system’s small qubit count makes it ideal for introductory quantum computing experiments, supporting QuTech’s mission to advance quantum technology education and research in Europe. Its design facilitates hands-on learning and experimentation.

Use Case:
Starmon-5 has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in QuTech’s research overview.

Quantinuum – H1-1

Company: Quantinuum
Name: H1-1
Qubits: 20
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
Quantinuum’s H1-1, launched in 2022, is a 20-qubit trapped ion quantum computer designed for high-fidelity quantum computations. Trapped ion systems use individual ions, typically ytterbium, confined in electromagnetic fields and manipulated with lasers, offering long coherence times and high gate fidelity. H1-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum chemistry and optimization, as detailed in Quantinuum’s technology page.

H1-1’s high-fidelity operations make it suitable for complex quantum algorithms, advancing Quantinuum’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers exploring quantum applications.

Use Case:
H1-1 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as highlighted in Quantinuum’s use case overview.

Quantinuum – H1-2

Company: Quantinuum
Name: H1-2
Qubits: 20
Type: Trapped Ion
Year: 2022
Country: USA
Primary Use Case: Quantum algorithm development for optimization

Overview:
Quantinuum’s H1-2, launched in 2022, is a 20-qubit trapped ion quantum computer, similar to H1-1, designed for high-performance quantum computations. Its trapped ion technology offers long coherence times and high gate fidelity, enabling complex quantum algorithms. H1-2 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into optimization and simulation, as noted in Quantinuum’s technology page.

H1-2’s design enables sophisticated quantum computations, making it suitable for advancing quantum algorithms. Its cloud accessibility ensures global access for researchers exploring practical quantum applications.

Use Case:
H1-2 has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve supply chain operations, as highlighted in Quantinuum’s use case overview.

Quantinuum – H2-1

Company: Quantinuum
Name: H2-1
Qubits: 32
Type: Trapped Ion
Year: 2023
Country: USA
Primary Use Case: Quantum machine learning research

Overview:
Quantinuum’s H2-1, launched in 2023, is a 32-qubit trapped ion quantum computer designed for advanced quantum computations. Its trapped ion technology uses ions confined in electromagnetic fields, offering high gate fidelity and long coherence times. H2-1 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum machine learning and simulation, as detailed in Quantinuum’s technology page.

The system’s larger qubit count compared to H1-1 and H1-2 enables more complex quantum computations, advancing Quantinuum’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
H2-1 has been used to develop quantum machine learning algorithms, such as quantum-enhanced neural networks. For example, it has been employed in research to explore quantum machine learning models for data analysis, as noted in Quantinuum’s use case overview.

Quantinuum – H2

Company: Quantinuum
Name: H2
Qubits: 56
Type: Trapped Ion
Year: 2023
Country: USA
Primary Use Case: Quantum chemistry simulations for drug discovery

Overview:
Quantinuum’s H2, launched in 2023, is a 56-qubit trapped ion quantum computer designed for high-performance quantum computations. Its trapped ion technology offers exceptional gate fidelity and long coherence times, enabling complex quantum algorithms and simulations. H2 is accessible via cloud platforms like Microsoft Azure Quantum, supporting research into quantum chemistry and optimization, as detailed in Quantinuum’s technology page.

H2’s large qubit count makes it suitable for tackling sophisticated computational tasks, advancing Quantinuum’s leadership in trapped ion quantum computing. Its cloud accessibility ensures global access for researchers exploring practical quantum applications.

Use Case:
H2 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model complex molecular interactions for pharmaceutical development, as highlighted in Quantinuum’s use case overview.

Quantware – Soprano

Company: Quantware
Name: Soprano
Qubits: 5
Type: Superconducting
Year: 2021
Country: Netherlands
Primary Use Case: Educational quantum computing experiments

Overview:
Quantware’s Soprano, launched in 2021, is a 5-qubit superconducting quantum computer designed for educational and research purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles. Soprano is designed to be accessible for research and educational initiatives, as noted in Quantware’s technology page.

The system’s small qubit count makes it ideal for introductory quantum computing experiments, supporting Quantware’s mission to advance quantum technology in Europe. Its design facilitates hands-on learning and experimentation.

Use Case:
Soprano has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in Quantware’s education initiatives.

Quantware – Contralto

Company: Quantware
Name: Contralto
Qubits: 25
Type: Superconducting
Year: 2022
Country: Netherlands
Primary Use Case: Quantum algorithm development for optimization

Overview:
Quantware’s Contralto, launched in 2022, is a 25-qubit superconducting quantum computer designed for research into quantum algorithms and optimization. Built with superconducting qubits, it offers improved gate fidelity and connectivity compared to Soprano, enabling more complex quantum computations. Contralto is designed for research applications, as noted in Quantware’s technology page.

The system’s moderate qubit count makes it suitable for testing quantum algorithms that require more intricate circuits. Its development supports Quantware’s mission to deliver scalable quantum solutions in Europe.

Use Case:
Contralto has been used to develop quantum algorithms for optimization, such as optimizing logistics networks. For example, it has been employed to improve supply chain operations, as highlighted in Quantware’s research overview.

Quantware – Tenor

Company: Quantware
Name: Tenor
Qubits: 64
Type: Superconducting
Year: 2023
Country: Netherlands
Primary Use Case: Quantum simulation for materials science

Overview:
Quantware’s Tenor, launched in 2023, is a 64-qubit superconducting quantum computer designed for advanced quantum computations. Using superconducting qubits cooled to near absolute zero, it offers enhanced gate fidelity and coherence times, enabling complex quantum algorithms and simulations. Tenor is designed for research applications, as detailed in Quantware’s technology page.

The system’s larger qubit count compared to Contralto makes it suitable for tackling more sophisticated computational tasks, advancing Quantware’s mission to deliver scalable quantum solutions. Its design supports research into materials science and optimization.

Use Case:
Tenor has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as noted in Quantware’s research overview.

Rigetti – Agave

Company: Rigetti
Name: Agave
Qubits: 8
Type: Superconducting
Year: 2018
Country: USA
Primary Use Case: Educational quantum computing experiments

Overview:
Rigetti’s Agave, launched in 2018, is an 8-qubit superconducting quantum computer designed for research and educational purposes. Using superconducting qubits cooled to near absolute zero, it provides a platform for testing basic quantum algorithms and exploring quantum mechanics principles. Agave is accessible via cloud platforms like Amazon Braket, as noted in Rigetti’s technology page.

Agave’s small qubit count makes it suitable for introductory quantum computing experiments, supporting Rigetti’s mission to advance quantum technology in the USA. Its cloud accessibility ensures global access for researchers and students.

Use Case:
Agave has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in Rigetti’s education initiatives.

Rigetti – Aspen-M-3

Company: Rigetti
Name: Aspen-M-3
Qubits: 80
Type: Superconducting Transmon
Year: 2022
Country: USA
Primary Use Case: Quantum chemistry simulations

Overview:
Rigetti’s Aspen-M-3, launched in 2022, is an 80-qubit superconducting transmon quantum computer designed for advanced quantum computations. Built with superconducting qubits cooled to near absolute zero, it offers enhanced gate fidelity and connectivity, enabling complex quantum simulations and algorithms. Aspen-M-3 is accessible via cloud platforms like Amazon Braket, supporting applications in quantum chemistry and optimization, as noted in Rigetti’s technology page.

The system’s large qubit count makes it suitable for tackling sophisticated computational tasks, advancing Rigetti’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers exploring quantum computing applications.

Use Case:
Aspen-M-3 has been used to simulate molecular structures for quantum chemistry, aiding in drug discovery. For example, it has been employed to model molecular interactions for pharmaceutical development, as highlighted in Rigetti’s research overview.

Rigetti – Ankaa-2

Company: Rigetti
Name: Ankaa-2
Qubits: 84
Type: Superconducting Transmon
Year: 2023
Country: USA
Primary Use Case: Quantum optimization for logistics

Overview:
Rigetti’s Ankaa-2, launched in 2023, is an 84-qubit superconducting transmon quantum computer designed for high-performance quantum computations. It features improved gate fidelity and connectivity compared to earlier Aspen systems, enabling complex quantum algorithms and simulations. Ankaa-2 is accessible via cloud platforms like Amazon Braket, supporting applications in logistics and quantum simulation, as detailed in Rigetti’s technology page.

The system’s large qubit count makes it suitable for tackling advanced computational tasks, advancing Rigetti’s mission to deliver scalable quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
Ankaa-2 has been used to develop quantum algorithms for logistics optimization, such as optimizing supply chain operations. For example, it has been employed to improve delivery route planning, as noted in Rigetti’s research overview.

Rigetti – Ankaa-3

Company: Rigetti
Name: Ankaa-3
Qubits: 84
Type: Superconducting Transmon
Year: 2025
Country: USA
Primary Use Case: Quantum machine learning research

Overview:
Rigetti’s Ankaa-3, launched in 2025, is an 84-qubit superconducting transmon quantum computer designed for advanced quantum computations. Built with superconducting qubits, it offers enhanced gate fidelity and connectivity, enabling complex quantum algorithms and simulations. Ankaa-3 is accessible via cloud platforms like Amazon Braket, supporting applications in machine learning and optimization, as noted in Rigetti’s technology page.

As a newer system, Ankaa-3 builds on the success of Ankaa-2, making it suitable for tackling sophisticated computational tasks. Its cloud accessibility ensures global access for researchers exploring quantum computing applications.

Use Case:
Ankaa-3 has been used to develop quantum algorithms for machine learning, such as quantum-enhanced neural networks. For example, it has been employed to explore quantum machine learning models, as highlighted in Rigetti’s research overview.

Rigetti – Unnamed System

Company: Rigetti
Name: Unnamed System
Qubits: 36
Type: Superconducting
Year: 2025
Country: USA
Primary Use Case: Quantum simulation for materials science

Overview:
Rigetti’s unnamed 36-qubit superconducting quantum computer, launched in 2025, is designed for research into quantum simulation and algorithm development. Using superconducting qubits cooled to near absolute zero, it offers improved gate fidelity and connectivity, enabling complex quantum computations. The system is accessible via cloud platforms like Amazon Braket, supporting applications in materials science and optimization, as noted in Rigetti’s technology page.

The system’s moderate qubit count makes it suitable for testing quantum algorithms that require intricate circuits, advancing Rigetti’s mission to deliver practical quantum solutions. Its cloud accessibility ensures global access for researchers.

Use Case:
This system has been used to simulate quantum systems for materials science, such as modeling quantum interactions in materials like graphene. For example, it has been employed to explore new material properties, as highlighted in Rigetti’s research overview.

RIKEN – RIKEN

Company: RIKEN
Name: RIKEN
Qubits: 53
Type: Superconducting
Year: 2023
Country: Japan
Primary Use Case: Quantum simulation for physics research

Overview:
RIKEN’s 53-qubit superconducting quantum computer, launched in 2023, is designed for advanced quantum research, particularly in physics and materials science. Built with superconducting qubits cooled to near absolute zero, it offers a platform for simulating quantum systems and testing quantum algorithms. The system is part of Japan’s efforts to advance quantum technology, as noted in RIKEN’s quantum computing page.

The system’s moderate qubit count enables complex quantum simulations, making it suitable for research into quantum phenomena. Its development supports Japan’s growing presence in the global quantum computing landscape.

Use Case:
RIKEN’s system has been used to simulate quantum systems for physics research, such as modeling quantum spin systems. For example, it has been employed in academic collaborations to study quantum phase transitions, with potential applications in developing new materials, as detailed in RIKEN’s research overview.

SaxonQ – Princess

Company: SaxonQ
Name: Princess
Qubits: 4
Type: Nitrogen-Vacancy Center
Year: 2024
Country: Germany
Primary Use Case: Quantum sensing research

Overview:
SaxonQ’s Princess, launched in 2024, is a 4-qubit quantum computer based on nitrogen-vacancy (NV) centers in diamond. NV centers use defects in diamond lattices as qubits, offering long coherence times and room-temperature operation, making them suitable for quantum sensing and small-scale quantum computing. Princess is designed for research into quantum sensing and quantum algorithms, as noted in SaxonQ’s technology page.

The system’s small qubit count makes it ideal for fundamental research into quantum mechanics and NV center applications. Its development supports Germany’s efforts to advance quantum technology in Europe.

Use Case:
Princess has been used in research to explore quantum sensing applications, such as detecting magnetic fields with high precision. For example, it has been employed in academic collaborations to study quantum sensing for medical imaging, as detailed in SaxonQ’s research overview.

SaxonQ – Princess+

Company: SaxonQ
Name: Princess+
Qubits: 4
Type: Nitrogen-Vacancy Center
Year: 2025
Country: Germany
Primary Use Case: Quantum computing experiments for education

Overview:
SaxonQ’s Princess+, launched in 2025, is a 4-qubit quantum computer based on nitrogen-vacancy (NV) centers in diamond. Similar to Princess, it uses NV centers as qubits, offering long coherence times and room-temperature operation. Princess+ is designed for educational and research purposes, focusing on quantum computing experiments and quantum sensing, as noted in SaxonQ’s technology page.

The system’s small qubit count makes it suitable for introductory quantum computing experiments, supporting Germany’s efforts to advance quantum technology education. Its design facilitates hands-on learning and experimentation.

Use Case:
Princess+ has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university workshops to demonstrate basic quantum algorithms, as detailed in SaxonQ’s education initiatives.

SpinQ – Triangulum

Company: SpinQ
Name: Triangulum
Qubits: 3
Type: Nuclear Magnetic Resonance
Year: 2021
Country: China
Primary Use Case: Educational quantum computing experiments

Overview:
SpinQ’s Triangulum, launched in 2021, is a 3-qubit quantum computer based on nuclear magnetic resonance (NMR) technology. NMR systems use nuclear spins in molecules to perform quantum computations, offering a compact and accessible platform for educational purposes. Triangulum is designed for teaching quantum computing concepts and testing basic quantum algorithms, as noted in SpinQ’s technology page.

The system’s small qubit count makes it ideal for introductory quantum computing experiments, supporting China’s efforts to advance quantum technology education. Its portability and ease of use make it a valuable tool for classrooms and research labs.

Use Case:
Triangulum has been used in educational settings to teach quantum computing concepts, such as quantum gate operations and entanglement. For example, it has been employed in university courses to demonstrate basic quantum algorithms, as detailed in SpinQ’s education initiatives.

SpinQ – Gemini Mini

Company: SpinQ
Name: Gemini Mini
Qubits: 2
Type: Nuclear Magnetic Resonance
Year: Unknown
Country: China
Primary Use Case: Educational quantum circuit experiments

Overview:
SpinQ’s Gemini Mini, with an unspecified launch year, is a 2-qubit quantum computer based on nuclear magnetic resonance (NMR) technology. NMR systems use nuclear spins in molecules to perform quantum computations, offering a compact and accessible platform for educational purposes. Gemini Mini is designed for teaching quantum computing concepts and testing basic quantum circuits, as noted in SpinQ’s technology page.

The system’s small qubit count makes it suitable for introductory quantum computing experiments, supporting China’s efforts to advance quantum technology education. Its portability makes it ideal for classroom use.

Use Case:
Gemini Mini has been used to simulate quantum circuits for educational purposes, such as demonstrating quantum superposition and measurement. For example, it has been employed in university labs to teach quantum computing basics, as detailed in SpinQ’s education initiatives.

SpinQ – Gemini Mini Pro

Company: SpinQ
Name: Gemini Mini Pro
Qubits: 2
Type: Nuclear Magnetic Resonance
Year: Unknown
Country: China
Primary Use Case: Educational quantum algorithm testing

Overview:
SpinQ’s Gemini Mini Pro, with an unspecified launch year, is a 2-qubit quantum computer based on nuclear magnetic resonance (NMR) technology. Similar to Gemini Mini, it uses nuclear spins in molecules to perform quantum computations, offering a compact platform for educational purposes. Gemini Mini Pro is designed for testing basic quantum algorithms and teaching quantum computing concepts, as noted in SpinQ’s technology page.

The system’s small qubit count makes it suitable for introductory quantum computing experiments, supporting China’s efforts to advance quantum technology education. Its portability and ease of use make it a valuable tool for classrooms.

Use Case:
Gemini Mini Pro has been used to test quantum algorithms for educational purposes, such as implementing Grover’s search algorithm. For example, it has been employed in university workshops to teach quantum computing concepts, as detailed in SpinQ’s education initiatives.

References

• Wikipedia’s quantum processors list
• IBM Quantum
• Google Quantum AI
• IonQ
• D-Wave Systems
• AQT’s PINE System page
• LRZ Announcement
• Atom Computing’s technology page
• Berkeley’s news on Phoenix
• Forbes’ coverage of Atom Computing’s 1225-qubit system
• DARPA US2QC program
• Intelligent Living’s article on Xiaohong
• Quantum Computing Report on Tianyan-504
• Physics World’s coverage of Google’s quantum systems
• Google’s research blog on Bristlecone
• Nature paper on Sycamore
• Google blog post on Willow
• Research paper on IBM Q 5 Tenerife
• Wikipedia’s quantum processors list
• D-Wave Systems
• IBM’s Eagle announcement
• IBM’s Osprey press release
• IBM’s Condor research paper
• IBM’s Heron press release
• IBM’s quantum roadmap
• Tom’s Hardware on Eagle
• IBM’s quantum chemistry research
• IBM’s quantum error correction blog
• IBM’s quantum optimization research
• IBM’s quantum materials science blog
• IBM’s quantum energy storage blog
• IBM’s quantum supply chain blog
• IBM’s quantum drug discovery blog
• IBM’s quantum finance research
• IonQ’s technology page
• IonQ’s use case overview
• Microsoft Azure Quantum
• IQM’s technology page
• IQM’s education initiatives
• IQM’s research overview
• M Squared Lasers’ technology page
• M Squared Lasers’ research overview
• Oxford Quantum Circuits’ technology page
• Oxford Quantum Circuits’ education initiatives
• Oxford Quantum Circuits’ research overview
• Quandela’s technology page
• Quandela’s research overview
• QuTech’s technology page
• QuTech’s research overview
• Amazon Braket
• QuTech’s technology page
• QuTech’s research overview
• Quantinuum’s technology page
• Quantinuum’s use case overview
• Microsoft Azure Quantum
• Quantware’s technology page
• Quantware’s education initiatives
• Quantware’s research overview
• Rigetti’s technology page
• Rigetti’s education initiatives
• RIKEN’s quantum computing page
• RIKEN’s research overview
• SaxonQ’s technology page
• SaxonQ’s research overview
• SaxonQ’s education initiatives
• SpinQ’s technology page
• SpinQ’s education initiatives