List of quantum processors

Last updated

This list contains quantum processors, also known as quantum processing units (QPUs). Some devices listed below have only been announced at press conferences so far, with no actual demonstrations or scientific publications characterizing the performance.

Contents

Quantum processors are difficult to compare due to the different architectures and approaches. Due to this, published qubit numbers do not reflect the performance levels of the processor. This is instead achieved through benchmarking metrics such as quantum volume, randomized benchmarking or circuit layer operations per second (CLOPS). [1]

Circuit-based quantum processors

These QPUs are based on the quantum circuit and quantum logic gate-based model of computing.

ManufacturerName/codename

designation

ArchitectureLayoutFidelity (%)Qubits (physical)Release dateQuantum volume
Alpine Quantum Technologies PINE System [2] Trapped ion 24 [3] June 7, 2021128 [4]
Atom Computing Phoenix Neutral atoms in optical lattices 100 [5] August 10, 2021
Atom Computing N/A Neutral atoms in optical lattices 1180 [6] [7] October 2023
Google N/A Superconducting N/A99.5 [8] 202017
Google N/A Superconducting 7×7 lattice99.7 [8] 49 [9] Q4 2017 (planned)
GoogleBristlecone Superconducting transmon 6×12 lattice99 (readout)
99.9 (1 qubit)
99.4 (2 qubits)		72 [10] [11] March 5, 2018
Google Sycamore Superconducting transmon 9×6 latticeN/A53 effective (54 total)2019
IBM IBM Q 5 Tenerife Superconducting bow tie99.897 (average gate)
98.64 (readout)		52016 [8]
IBM IBM Q 5 Yorktown Superconducting bow tie99.545 (average gate)
94.2 (readout)		5
IBMIBM Q 14 Melbourne Superconducting N/A99.735 (average gate)
97.13 (readout)		14
IBMIBM Q 16 Rüschlikon Superconducting 2×8 lattice99.779 (average gate)
94.24 (readout)		16 [12] May 17, 2017
(Retired: 26 September 2018) [13]
IBMIBM Q 17 Superconducting N/AN/A17 [12] May 17, 2017
IBMIBM Q 20 Tokyo Superconducting 5×4 lattice99.812 (average gate)
93.21 (readout)		20 [14] November 10, 2017
IBMIBM Q 20 Austin Superconducting 5×4 latticeN/A20(Retired: 4 July 2018) [13]
IBMIBM Q 50 prototype Superconducting transmonN/AN/A50 [14]
IBMIBM Q 53 Superconducting N/AN/A53October 2019
IBM IBM Eagle Superconducting N/AN/A127 [15] November 2021
IBM IBM Osprey [6] [7] Superconducting N/AN/A433 [15] November 2022
IBM IBM Condor [16] [6] Superconducting N/AN/A1121 [15] December 2023
IBM IBM Heron [16] [6] Superconducting N/AN/A133December 2023
IBMIBM Armonk [17] Superconducting Single QubitN/A1October 16, 2019
IBMIBM Ourense [17] Superconducting TN/A5July 3, 2019
IBMIBM Vigo [17] Superconducting TN/A5July 3, 2019
IBMIBM London [17] Superconducting TN/A5September 13, 2019
IBMIBM Burlington [17] Superconducting TN/A5September 13, 2019
IBMIBM Essex [17] Superconducting TN/A5September 13, 2019
IBMIBM Athens [18] Superconducting N/A532 [19]
IBMIBM Belem [18] Superconducting Falcon r4T [20] N/A516 [20]
IBMIBM Bogotá [18] Superconducting Falcon r4L [20] N/A532 [20]
IBMIBM Casablanca [18] Superconducting Falcon r4H [20] N/A7(Retired – March 2022)32 [20]
IBMIBM Dublin [18] Superconducting N/A2764
IBMIBM Guadalupe [18] Superconducting Falcon r4P [20] N/A1632 [20]
IBMIBM Kolkata Superconducting N/A27128
IBMIBM Lima [18] Superconducting Falcon r4T [20] N/A58 [20]
IBMIBM Manhattan [18] Superconducting N/A6532 [19]
IBMIBM Montreal [18] Superconducting Falcon r4 [20] N/A27128 [20]
IBMIBM Mumbai [18] Superconducting Falcon r5.1 [20] N/A27128 [20]
IBMIBM Paris [18] Superconducting N/A2732 [19]
IBMIBM Quito [18] Superconducting Falcon r4T [20] N/A516 [20]
IBMIBM Rome [18] Superconducting N/A532 [19]
IBMIBM Santiago [18] Superconducting N/A532 [19]
IBMIBM Sydney [18] Superconducting Falcon r4 [20] N/A2732 [20]
IBMIBM Toronto [18] Superconducting Falcon r4 [20] N/A2732 [20]
Intel 17-Qubit Superconducting Test Chip Superconducting 40-pin cross gapN/A17 [21] [22] October 10, 2017
Intel Tangle Lake Superconducting 108-pin cross gapN/A49 [23] January 9, 2018
IntelTunnel Falls Semiconductor spin qubits 12 [24] June 15, 2023
IonQ Harmony Trapped ion All-to-All [20] 11 [25] 20228 [20]
IonQ Aria Trapped ion All-to-All [20] 25 [25] 2022
IonQForteTrapped ion32x1 chain [26] All-to-All [20] 99.98 (1 qubit)
98.5–99.3 (2 qubit) [26] 		32 [25] 2022
IQM - Superconducting Star99.91 (1 qubit)
99.14 (2 qubits)		5 [27] November 30, 2021 [28] N/A
IQM - Superconducting Square lattice99.91 (1 qubit median)
99.944 (1 qubit max)
98.25 (2 qubits median)
99.1 (2 qubits max)		20October 9, 2023 [29] 16 [30]
M Squared Lasers Maxwell Neutral atoms in optical lattices 99.5 (3-qubit gate), 99.1 (4-qubit gate) [31] 200 [32] November 2022
Oxford Quantum Circuits Lucy [33] Superconducting82022
Oxford Quantum Circuits OQC Toshiko [34] Superconducting322023
Quandela Ascella Photonics N/A99.6 (1 qubit)
93.8 (2 qubits)
86.0 (3 qubits)		6 [35] 2022 [36]
QuTech at TU Delft Spin-2 Semiconductor spin qubits 99 (average gate)
85 (readout) [37] 		22020
QuTech at TU Delft - Semiconductor spin qubits 6 [38] September 2022
QuTech at TU DelftStarmon-5SuperconductingX configuration97 (readout) [39] 52020
Quantinuum H2 [40] Trapped ionRacetrack, All-to-All99.997 (1 qubit)
99.8 (2 qubit)		32May 9, 202365,536 [41]
Quantinuum H1-1 [42] Trapped ion15×15 (Circuit Size)99.996 (1 qubit)
99.914 (2 qubit)		2020221,048,576 [43]
QuantinuumH1-2 [42] Trapped ionAll-to-All [20] 99.996 (1 qubit)
99.7 (2 qubit)		1220224096 [44]
Quantware Soprano [45] Superconducting99.9 (single-qubit gates)5July 2021
Quantware Contralto [46] Superconducting99.9 (single-qubit gates)25March 7, 2022 [47]
QuantwareTenor [48] Superconducting64February 23, 2023
Rigetti AgaveSuperconductingN/A96 (Single-qubit gates)

87 (Two-qubit gates)

8June 4, 2018 [49]
Rigetti AcornSuperconducting transmonN/A98.63 (Single-qubit gates)

87.5 (Two-qubit gates)

19 [50] December 17, 2017
RigettiAspen-1SuperconductingN/A93.23 (Single-qubit gates)

90.84 (Two-qubit gates)

16November 30, 2018 [49]
RigettiAspen-4Superconducting99.88 (Single-qubit gates)

94.42 (Two-qubit gates)

13March 10, 2019
RigettiAspen-7Superconducting99.23 (Single-qubit gates)

95.2 (Two-qubit gates)

28November 15, 2019
RigettiAspen-8Superconducting99.22 (Single-qubit gates)

94.34 (Two-qubit gates)

31May 5, 2020
RigettiAspen-9Superconducting99.39 (Single-qubit gates)

94.28 (Two-qubit gates)

32February 6, 2021
RigettiAspen-10Superconducting99.37 (Single-qubit gates)

94.66 (Two-qubit gates)

32November 4, 2021
RigettiAspen-11SuperconductingOctagonal [20] 99.8 (Single-qubit gates) 92.7 (Two-qubit gates CZ) 91.0 (Two-qubit gates XY)40December 15, 2021
RigettiAspen-M-1Superconducting transmonOctagonal [20] 99.8 (Single-qubit gates) 93.7 (Two-qubit gates CZ) 94.6 (Two-qubit gates XY)80February 15, 20228 [20]
RigettiAspen-M-2Superconducting transmon99.8 (Single-qubit gates) 91.3 (Two-qubit gates CZ) 90.0 (Two-qubit gates XY)80August 1, 2022
RigettiAspen-M-3Superconducting transmonN/A99.9 (Single-qubit gates) 94.7 (Two-qubit gates CZ) 95.1 (Two-qubit gates XY)80 [51] December 2, 2022
RigettiAnkaa-2Superconducting transmonN/A98 (Two-qubit gates)84 [52] December 20, 2023
RIKEN RIKEN [53] SuperconductingN/AN/A53 effective (64 total) [54] [55] March 27, 2023N/A
SpinQ Triangulum Nuclear magnetic resonance 3 [56] September 2021
USTC Jiuzhang Photonics N/AN/A76 [57] [58] 2020
USTC ZuchongzhiSuperconductingN/AN/A62 [59] 2020
USTCZuchongzhi 2.1Superconductinglattice [60] 99.86 (Single-qubit gates) 99.41 (Two-qubit gates) 95.48 (Readout)66 [61] 2021
Xanadu Borealis [62] Photonics (Continuous-variable) N/AN/A216 [62] 2022 [62]
Xanadu X8 [63] Photonics (Continuous-variable) N/AN/A82020
XanaduX12Photonics (Continuous-variable) N/AN/A122020 [63]
XanaduX24Photonics (Continuous-variable) N/AN/A242020 [63]
CAS Xiaohong [64] SuperconductingN/AN/A504 [64] 2024

Annealing quantum processors

These QPUs are based on quantum annealing, not to be confused with digital annealing. [65]

ManufacturerName/Codename

/Designation

ArchitectureLayoutFidelity (%)QubitsRelease date
D-Wave D-Wave One (Rainier)SuperconductingC4 = Chimera(4,4,4) [66] = 4×4 K4,4 N/A128May 11, 2011
D-Wave D-Wave TwoSuperconductingC8 = Chimera(8,8,4) [66] = 8×8 K4,4 N/A5122013
D-Wave D-Wave 2XSuperconductingC12 = Chimera(12,12,4) [66] = 12×12 K4,4N/A11522015
D-Wave D-Wave 2000QSuperconductingC16 = Chimera(16,16,4) [66] = 16×16 K4,4N/A20482017
D-Wave D-Wave AdvantageSuperconductingPegasus P16 [67] N/A57602020
D-Wave D-Wave Advantage 2 [68] [69] [70] [71] Superconducting [68] [69] Zephyr Z15 [71] [72] N/A7000+ [68] [69] [70] [71] [72] Late 2024 either 2025 [68] [69] [70] [71] [72]

Analog quantum processors

These QPUs are based on analog Hamiltonian simulation.

ManufacturerName/Codename/DesignationArchitectureLayoutFidelity (%)QubitsRelease date
QuEra AquilaNeutral atomsN/AN/A256 [73] November 2022

See also

Related Research Articles

<span class="mw-page-title-main">Quantum computing</span> Technology that uses quantum mechanics

A quantum computer is a computer that takes advantage of quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states.

This is a timeline of quantum computing.

Superconducting quantum computing is a branch of solid state quantum computing that implements superconducting electronic circuits using superconducting qubits as artificial atoms, or quantum dots. For superconducting qubits, the two logic states are the ground state and the excited state, denoted respectively. Research in superconducting quantum computing is conducted by companies such as Google, IBM, IMEC, BBN Technologies, Rigetti, and Intel. Many recently developed QPUs use superconducting architecture.

Quantum programming is the process of designing or assembling sequences of instructions, called quantum circuits, using gates, switches, and operators to manipulate a quantum system for a desired outcome or results of a given experiment. Quantum circuit algorithms can be implemented on integrated circuits, conducted with instrumentation, or written in a programming language for use with a quantum computer or a quantum processor.

Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions, by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete with many local minima; such as finding the ground state of a spin glass or the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori in 1998 though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.

<span class="mw-page-title-main">D-Wave Systems</span> Canadian quantum computing company

D-Wave Quantum Systems Inc. is a Canadian quantum computing company, based in Burnaby, British Columbia. D-Wave claims to be the world's first company to sell computers that exploit quantum effects in their operation. D-Wave's early customers include Lockheed Martin, the University of Southern California, Google/NASA, and Los Alamos National Lab.

Adiabatic quantum computation (AQC) is a form of quantum computing which relies on the adiabatic theorem to perform calculations and is closely related to quantum annealing.

Daniel Amihud Lidar is the holder of the Viterbi Professorship of Engineering at the University of Southern California, where he is a professor of electrical engineering, chemistry, physics & astronomy. He is the director and co-founder of the USC Center for Quantum Information Science & Technology (CQIST), the director of the USC-IBM Quantum Innovation Center, as well as scientific director of the USC-Lockheed Martin Quantum Computing Center, notable for his research on control of quantum systems and quantum information processing.

D-Wave Two is the second commercially available quantum computer, and the successor to the first commercially available quantum computer, D-Wave One. Both computers were developed by Canadian company D-Wave Systems. The computers are not general purpose, but rather are designed for quantum annealing. Specifically, the computers are designed to use quantum annealing to solve a single type of problem known as quadratic unconstrained binary optimization. As of 2015, it was still debated whether large-scale entanglement takes place in D-Wave Two, and whether current or future generations of D-Wave computers will have any advantage over classical computers.

<span class="mw-page-title-main">Quantum machine learning</span> Interdisciplinary research area at the intersection of quantum physics and machine learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs.

David P. DiVincenzo is an American theoretical physicist. He is the director of the Institute of Theoretical Nanoelectronics at the Peter Grünberg Institute at the Forschungszentrum Jülich and professor at the Institute for Quantum Information at RWTH Aachen University. With Daniel Loss, he proposed the Loss–DiVincenzo quantum computer in 1997, which would use electron spins in quantum dots as qubits.

IBM Quantum Platform is an online platform allowing public and premium access to cloud-based quantum computing services provided by IBM. This includes access to a set of IBM's prototype quantum processors, a set of tutorials on quantum computation, and access to an interactive textbook. As of February 2021, there are over 20 devices on the service, six of which are freely available for the public. This service can be used to run algorithms and experiments, and explore tutorials and simulations around what might be possible with quantum computing.

Cloud-based quantum computing is the invocation of quantum emulators, simulators or processors through the cloud. Increasingly, cloud services are being looked on as the method for providing access to quantum processing. Quantum computers achieve their massive computing power by initiating quantum physics into processing power and when users are allowed access to these quantum-powered computers through the internet it is known as quantum computing within the cloud.

In quantum computing, quantum supremacy or quantum advantage is the goal of demonstrating that a programmable quantum computer can solve a problem that no classical computer can solve in any feasible amount of time, irrespective of the usefulness of the problem. The term was coined by John Preskill in 2012, but the concept dates to Yuri Manin's 1980 and Richard Feynman's 1981 proposals of quantum computing.

<span class="mw-page-title-main">Rigetti Computing</span> American quantum computing company

Rigetti Computing, Inc. is a Berkeley, California-based developer of quantum integrated circuits used for quantum computers. The company also develops a cloud platform called Forest that enables programmers to write quantum algorithms.

Quil is a quantum instruction set architecture that first introduced a shared quantum/classical memory model. It was introduced by Robert Smith, Michael Curtis, and William Zeng in A Practical Quantum Instruction Set Architecture. Many quantum algorithms require a shared memory architecture. Quil is being developed for the superconducting quantum processors developed by Rigetti Computing through the Forest quantum programming API. A Python library called pyQuil was introduced to develop Quil programs with higher level constructs. A Quil backend is also supported by other quantum programming environments.

In quantum computing, a qubit is a unit of information analogous to a bit in classical computing, but it is affected by quantum mechanical properties such as superposition and entanglement which allow qubits to be in some ways more powerful than classical bits for some tasks. Qubits are used in quantum circuits and quantum algorithms composed of quantum logic gates to solve computational problems, where they are used for input/output and intermediate computations.

Quantum volume is a metric that measures the capabilities and error rates of a quantum computer. It expresses the maximum size of square quantum circuits that can be implemented successfully by the computer. The form of the circuits is independent from the quantum computer architecture, but compiler can transform and optimize it to take advantage of the computer's features. Thus, quantum volumes for different architectures can be compared.

The current state of quantum computing is referred to as the noisy intermediate-scale quantum (NISQ) era, characterized by quantum processors containing up to 1,000 qubits which are not advanced enough yet for fault-tolerance or large enough to achieve quantum advantage. These processors, which are sensitive to their environment (noisy) and prone to quantum decoherence, are not yet capable of continuous quantum error correction. This intermediate-scale is defined by the quantum volume, which is based on the moderate number of qubits and gate fidelity. The term NISQ was coined by John Preskill in 2018. In October 2023, the 1,000 qubit mark was passed for the first time by Atom Computing's 1,180 qubit quantum processor. However, as of 2024, only two quantum processors have over 1,000 qubits, with sub-1,000 quantum processors still remaining the norm.

This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.

References

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