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 physical qubit numbers do not reflect the performance levels of the processor. This is instead achieved through the number of logical qubits or 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 TechnologiesPINE System [2] Trapped ion 24 [3] June 7, 2021128 [4]
Atom ComputingPhoenix Neutral atoms in optical lattices 100 [5] August 10, 2021
Atom ComputingN/A Neutral atoms in optical lattices 35×35 lattice (with 45 vacancies)< 99.5 (2 qubits) [6] 1180 [7] [8] October 2023
Google N/A Superconducting N/A99.5 [9] 202017
Google N/A Superconducting 7×7 lattice99.7 [9] 49 [10] Q4 2017 (planned)
GoogleBristlecone Superconducting transmon 6×12 lattice99 (readout)
99.9 (1 qubit)
99.4 (2 qubits)		72 [11] [12] March 5, 2018
Google Sycamore Superconducting transmon 9×6 latticeN/A53 effective (54 total)2019
Google Willow Superconducting transmon 7×7 lattice99.965% (1-qubit)
99.67% (2-qubit)
Surface code error correction implemented.		49 effective (105 total)December 9, 2024 [13]
IBM IBM Q 5 Tenerife Superconducting bow tie99.897 (average gate)
98.64 (readout)		52016 [9]
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 [14] May 17, 2017
(Retired: 26 September 2018) [15]
IBMIBM Q 17 Superconducting N/AN/A17 [14] May 17, 2017
IBMIBM Q 20 Tokyo Superconducting 5×4 lattice99.812 (average gate)
93.21 (readout)		20 [16] November 10, 2017
IBMIBM Q 20 Austin Superconducting 5×4 latticeN/A20(Retired: 4 July 2018) [15]
IBMIBM Q 50 prototype Superconducting transmonN/AN/A50 [16]
IBMIBM Q 53 Superconducting N/AN/A53October 2019
IBM IBM Eagle Superconducting transmonN/AN/A127 [17] November 2021
IBM IBM Osprey [7] [8] Superconducting N/AN/A433 [17] November 2022
IBM IBM Condor [18] [7] Superconducting Honeycomb [19] N/A1121 [17] December 2023
IBM IBM Heron [18] [7] Superconducting N/AN/A133December 2023
IBMIBM Heron R2 [20] Superconducting Heavy hex96.5 (2 qubits)156November 2024
IBMIBM Armonk [21] Superconducting Single QubitN/A1October 16, 2019
IBMIBM Ourense [21] Superconducting TN/A5July 3, 2019
IBMIBM Vigo [21] Superconducting TN/A5July 3, 2019
IBMIBM London [21] Superconducting TN/A5September 13, 2019
IBMIBM Burlington [21] Superconducting TN/A5September 13, 2019
IBMIBM Essex [21] Superconducting TN/A5September 13, 2019
IBMIBM Athens [22] Superconducting N/A532 [23]
IBMIBM Belem [22] Superconducting Falcon r4T [24] N/A516 [24]
IBMIBM Bogotá [22] Superconducting Falcon r4L [24] N/A532 [24]
IBMIBM Casablanca [22] Superconducting Falcon r4H [24] N/A7(Retired – March 2022)32 [24]
IBMIBM Dublin [22] Superconducting N/A2764
IBMIBM Guadalupe [22] Superconducting Falcon r4P [24] N/A1632 [24]
IBMIBM Kolkata Superconducting N/A27128
IBMIBM Lima [22] Superconducting Falcon r4T [24] N/A58 [24]
IBMIBM Manhattan [22] Superconducting N/A6532 [23]
IBMIBM Montreal [22] Superconducting Falcon r4 [24] N/A27128 [24]
IBMIBM Mumbai [22] Superconducting Falcon r5.1 [24] N/A27128 [24]
IBMIBM Paris [22] Superconducting N/A2732 [23]
IBMIBM Quito [22] Superconducting Falcon r4T [24] N/A516 [24]
IBMIBM Rome [22] Superconducting N/A532 [23]
IBMIBM Santiago [22] Superconducting N/A532 [23]
IBMIBM Sydney [22] Superconducting Falcon r4 [24] N/A2732 [24]
IBMIBM Toronto [22] Superconducting Falcon r4 [24] N/A2732 [24]
Intel 17-Qubit Superconducting Test Chip Superconducting 40-pin cross gapN/A17 [25] [26] October 10, 2017
Intel Tangle Lake Superconducting 108-pin cross gapN/A49 [27] January 9, 2018
IntelTunnel Falls Semiconductor spin qubits 12 [28] June 15, 2023
IonQ Harmony Trapped ion All-to-All [24] 99.73 (1 qubit)

90.02 (2 qubit) 99.30 (SPAM)

11 [29] 20228 [24]
IonQ AriaTrapped ionAll-to-All [24] 99.97 (1 qubit)

98.33 (2 qubit) 98.94 ((SPAM)

25 [29] 2022
IonQForteTrapped ion366x1 chain [30] All-to-All [24] 99.98 (1 qubit)
98.5–99.3 (2 qubit) [30] 99.56 ((SPAM)		36 [29] (earlier 32)2022
IQM- Superconducting Star99.91 (1 qubit)
99.14 (2 qubits)		5 [31] November 30, 2021 [32] 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 [33] 16 [34]
M Squared Lasers MaxwellNeutral atoms in optical lattices99.5 (3-qubit gate), 99.1 (4-qubit gate) [35] 200 [36] November 2022
Oxford Quantum CircuitsLucy [37] Superconducting82022
Oxford Quantum CircuitsOQC Toshiko [38] Superconducting322023
Quandela Ascella Photonics N/A99.6 (1 qubit)
93.8 (2 qubits)
86.0 (3 qubits)		6 [39] 2022 [40]
QuTech at TU Delft Spin-2 Semiconductor spin qubits 99 (average gate)
85 (readout) [41] 		22020
QuTech at TU Delft -Semiconductor spin qubits6 [42] September 2022
QuTech at TU DelftStarmon-5SuperconductingX configuration97 (readout) [43] 52020
Quantinuum H2 [44] Trapped ionRacetrack, All-to-All99.997 (1 qubit)
99.87 (2 qubit)		56 [45] (earlier 32)May 9, 20232,097,152 [46]
Quantinuum H1-1 [47] Trapped ion15×15 (Circuit Size)99.996 (1 qubit)
99.914 (2 qubit)		2020221,048,576 [48]
Quantinuum H1-2 [47] Trapped ionAll-to-All [24] 99.996 (1 qubit)
99.7 (2 qubit)		1220224096 [49]
QuantwareSoprano [50] Superconducting99.9 (single-qubit gates)5July 2021
QuantwareContralto [51] Superconducting99.9 (single-qubit gates)25March 7, 2022 [52]
QuantwareTenor [53] Superconducting64February 23, 2023
Rigetti AgaveSuperconductingN/A96 (Single-qubit gates)

87 (Two-qubit gates)

8June 4, 2018 [54]
Rigetti AcornSuperconducting transmonN/A98.63 (Single-qubit gates)

87.5 (Two-qubit gates)

19 [55] December 17, 2017
Rigetti Aspen-1SuperconductingN/A93.23 (Single-qubit gates)

90.84 (Two-qubit gates)

16November 30, 2018 [54]
Rigetti Aspen-4Superconducting99.88 (Single-qubit gates)

94.42 (Two-qubit gates)

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

95.2 (Two-qubit gates)

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

94.34 (Two-qubit gates)

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

94.28 (Two-qubit gates)

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

94.66 (Two-qubit gates)

32November 4, 2021
Rigetti Aspen-11SuperconductingOctagonal [24] 99.8 (Single-qubit gates) 92.7 (Two-qubit gates CZ) 91.0 (Two-qubit gates XY)40December 15, 2021
Rigetti Aspen-M-1Superconducting transmonOctagonal [24] 99.8 (Single-qubit gates) 93.7 (Two-qubit gates CZ) 94.6 (Two-qubit gates XY)80February 15, 20228 [24]
Rigetti Aspen-M-2Superconducting transmon99.8 (Single-qubit gates) 91.3 (Two-qubit gates CZ) 90.0 (Two-qubit gates XY)80August 1, 2022
Rigetti Aspen-M-3Superconducting transmonN/A99.9 (Single-qubit gates) 94.7 (Two-qubit gates CZ) 95.1 (Two-qubit gates XY)80 [56] December 2, 2022
Rigetti Ankaa-2Superconducting transmonN/A98 (Two-qubit gates)84 [57] December 20, 2023
RIKEN RIKEN [58] SuperconductingN/AN/A53 effective (64 total) [59] [60] March 27, 2023N/A
SaxonQPrincess Nitrogen-vacancy center 4 [61] June 26, 2024
SpinQTriangulum Nuclear magnetic resonance 3 [62] September 2021
USTC Jiuzhang Photonics N/AN/A76 [63] [64] 2020
USTC Zuchongzhi Superconducting N/AN/A62 [65] 2020
USTC Zuchongzhi 2.1 Superconducting lattice [66] 99.86 (Single-qubit gates) 99.41 (Two-qubit gates) 95.48 (Readout)66 [67] 2021
USTC Zuchongzhi 3.0 [68] Superconducting transmon15 x 799.90 (Single-qubit gates) 99.62 (Two-qubit gates) 99.18 (Readout)105December 16, 2024
Xanadu Borealis [69] Photonics (Continuous-variable) N/AN/A216 [69] 2022 [69]
Xanadu X8 [70] Photonics (Continuous-variable) N/AN/A82020
Xanadu X12Photonics (Continuous-variable)N/AN/A122020 [70]
Xanadu X24Photonics (Continuous-variable)N/AN/A242020 [70]
CAS Xiaohong [71] SuperconductingN/AN/A504 [71] 2024

Annealing quantum processors

These QPUs are based on quantum annealing, not to be confused with digital annealing. [72]

ManufacturerName/Codename

/Designation

ArchitectureLayoutFidelity (%)QubitsRelease date
D-Wave D-Wave One (Rainier)SuperconductingC4 = Chimera(4,4,4) [73] = 4×4 K4,4 N/A128May 11, 2011
D-Wave D-Wave TwoSuperconductingC8 = Chimera(8,8,4) [73] = 8×8 K4,4 N/A5122013
D-WaveD-Wave 2XSuperconductingC12 = Chimera(12,12,4) [73] = 12×12 K4,4N/A11522015
D-WaveD-Wave 2000QSuperconductingC16 = Chimera(16,16,4) [73] = 16×16 K4,4N/A20482017
D-WaveD-Wave AdvantageSuperconductingPegasus P16 [74] N/A57602020
D-Wave D-Wave Advantage 2 [75] [76] [77] [78] Superconducting [75] [76] Zephyr Z15 [78] [79] N/A7440 [80] 2024 [75] [76] [77] [78] [79]

Analog quantum processors

These QPUs are based on analog Hamiltonian simulation.

ManufacturerName/Codename/DesignationArchitectureLayoutFidelity (%)QubitsRelease date
QuEra AquilaNeutral atomsN/AN/A256 [81] November 2022

See also

Related Research Articles

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

A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. Theoretically a large-scale quantum computer could break some widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.

<span class="mw-page-title-main">Timeline of quantum computing and communication</span>

This is a timeline of quantum computing.

In logic circuits, the Toffoli gate, also known as the CCNOT gate (“controlled-controlled-not”), invented by Tommaso Toffoli, is a CNOT gate with two control qubits and one target qubit. That is, the target qubit will be inverted if the first and second qubits are both 1. It is a universal reversible logic gate, which means that any classical reversible circuit can be constructed from Toffoli gates.

Superconducting quantum computing is a branch of solid state physics and 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 solving 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 quantum computing company with locations in Palo Alto, California and 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 Laboratory.

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.

The USC-Lockheed Martin Quantum Computing Center (QCC) is a joint scientific research effort between Lockheed Martin Corporation and the University of Southern California (USC). The QCC is housed at the Information Sciences Institute (ISI), a computer science and engineering research unit of the USC Viterbi School of Engineering, and is jointly operated by ISI and Lockheed Martin.

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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 2011, 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. Rigetti also develops a cloud platform called Forest that enables programmers to write quantum algorithms.

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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.

Quantinuum is a quantum computing company formed by the merger of Cambridge Quantum and Honeywell Quantum Solutions. The company's H-Series trapped-ion quantum computers set the highest quantum volume to date of 1,048,576 in April 2024. This architecture supports all-to-all qubit connectivity, allowing entangled states to be created between all qubits, and enables a high fidelity of quantum states.

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.

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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