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Quantum supremacy using a programmable superconducting processor
Open accessAbstract
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2–7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8–14 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
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“In typical cases, if noise overwhelms the circuit, the measured distribution will be uniform18 and the measured XEB will be 0.”
From Logical quantum processor based on reconfigurable atom arrays (2024) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As an illustration, a type of superconducting processor used for Google's quantum supremacy experiment had single-qubit and two-qubit gate errors of 0.1% and 0.3%, respectively [5].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [5]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“Recent advances in control and readout of quantum bits have underscored the spectacular promise of QIP [82,83].”
From 2022 Roadmap on integrated quantum photonics (2022) · cites this paper as [82]
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“Quantum advantage was demonstrated against the best classical computers for the task of sampling the output of a pseudo-random quantum circuit.”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Arute et al., 2019
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“Finally, using 3D [[8,3,2]] code blocks16,17, we realize computationally complex sampling circuits18 with up to 48 logical qubits entangled with hypercube connectivity19 with 228 logical two-qubit gates and 48 logical CCZ gates20.”
From Logical quantum processor based on reconfigurable atom arrays (2024) · cites this paper as [18]
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“We obtain an XEB of approximately 0.1 for 48 logical qubits and hundreds of nonlocal logical entangling gates, up to roughly an order of magnitude higher than previous physical qubit implementations of digital circuits of similar complexity18,48, showing the benefits of a logical encoding for this application.”
From Logical quantum processor based on reconfigurable atom arrays (2024) · cites this paper as [18]
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In the citing paper
“The researchers have explained that their sycamore processor takes about 200s to compute. A similar job would take approximately 10,000years by a state-of-the-art classical supercomputer [5].”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [5]
The cited source says what the claim attributes to it.
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“In the year of 2019, researchers executed quantum supremacy successfully in a 53 qubit superconducting machine.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [5]
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“RCS is a system-level benchmark assessing how effectively a quantum computer can generate computationally complex quantum states15.”
From A 98-qubit trapped-ion quantum computer with all-to-all connectivity (2026) · cites this paper as [15]
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“In either case, the quality of samples can be judged by statistical tests, with the linear cross-entropy test being a widely used standard15.”
From A 98-qubit trapped-ion quantum computer with all-to-all connectivity (2026) · cites this paper as [15]
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“Existing quantum devices contain on the order of 100 physical qubits.”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Arute et al., 2019
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In the citing paper
“The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing.”
From Quantum Computing: Vision and Challenges (2025) · cites this paper as [4]
In the citing paper
“Error correction requires thousands of physical qubits for each logical qubit [91], resulting in millions of qubits for calculating the FeMoco ground state energy [45, 46].”
From Drug design on quantum computers (2024) · cites this paper as [45]
In the citing paper
“However, in the near term, this tradeoff is a happy one as many NISQ architectures—most notably superconducting qubit devices—can easily and rapidly repeat executions of a quantum circuit.”
From Training deep quantum neural networks (2020) · cites this paper as [46]
In the citing paper
“For example, the recently reported experiment involving the “Sycamore” quantum computer executed one instance of a quantum circuit a million times in 200 s46.”
From Training deep quantum neural networks (2020) · cites this paper as [46]
In the citing paper
“For comparison, the recent work on quantum supremacy by Google reported fidelities of 0.1% for single-qubit gates and 0.3% for two-qubit gates in a superconducting processor [20].”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [20]
In the citing paper
“Given that the current record for a controllable, programmable quantum processor is 53 qubits [20], there is still a long way to go.”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [20]
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Universal control of a six-qubit quantum processor in silicon
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Superconducting quantum computing: a review
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High-fidelity entanglement and detection of alkaline-earth Rydberg atoms
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Quantum simulation and computing with Rydberg-interacting qubits
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A Race-Track Trapped-Ion Quantum Processor
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Self-citationThe randomized measurement toolbox
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Quantum computer based on color centers in diamond
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Quantum annealing for industry applications: introduction and review
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Repeated quantum error detection in a surface code
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100,000-spin coherent Ising machine
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Time-crystalline eigenstate order on a quantum processor
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Self-citationQuantum walks on a programmable two-dimensional 62-qubit superconducting processor
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Robust data encodings for quantum classifiers
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Quantum Low-Density Parity-Check Codes
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Realization of an Error-Correcting Surface Code with Superconducting Qubits
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CMOS-based cryogenic control of silicon quantum circuits
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Controlling quantum many-body dynamics in driven Rydberg atom arraysPreprint
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Towards a distributed quantum computing ecosystem
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Intelligent Computing: The Latest Advances, Challenges, and Future
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Self-citationDemonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms
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Metadata
- Authors
- Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., Burkett, B., Chen, Y., Chen, Z., Chiaro, B., Collins, R., Courtney, W., Dunsworth, A., Farhi, E., Foxen, B., Fowler, A., Gidney, C., Giustina, M., Graff, R., Guerin, K., Habegger, S., Harrigan, M. P., Hartmann, M. J., Ho, A., Hoffmann, M., Huang, T., Humble, T. S., Isakov, S. V., Jeffrey, E., Jiang, Z., Kafri, D., Kechedzhi, K., Kelly, J., Klimov, P. V., Knysh, S., Korotkov, A., Kostritsa, F., Landhuis, D., Lindmark, M., Lucero, E., Lyakh, D., Mandrà, S., McClean, J. R., McEwen, M., Megrant, A., Mi, X., Michielsen, K., Mohseni, M., Mutus, J., Naaman, O., Neeley, M., Neill, C., Niu, M. Y., Ostby, E., Petukhov, A., Platt, J. C., Quintana, C., Rieffel, E. G., Roushan, P., Rubin, N. C., Sank, D., Satzinger, K. J., Smelyanskiy, V., Sung, K. J., Trevithick, M. D., Vainsencher, A., Villalonga, B., White, T., Yao, Z. J., Yeh, P., Zalcman, A., Neven, H., & Martinis, J. M.
- Affiliations
- Google (United States) (US)
- Ames Research Center (US)
- University of California, Santa Barbara (US)
- Oak Ridge National Laboratory (US)
- University of California, Riverside (US)
- Forschungszentrum Jülich (DE)
- Corresponding author
- Martinis, John M.
- ORCID
- Venue
- Nature
- Published
- 2019-10-23
- Volume
- 574
- Issue
- 7779
- Pages
- 505-510
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2982169647
- Cited by
- 7,720
- References
- 51
- Citations by year
- 2026: 701 · 2025: 1157 · 2024: 1143 · 2023: 1234 · 2022: 1230 · 2021: 1315 · 2020: 875 · 2019: 60
- FWCI
- 397.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, other-oa)
- arXiv (Cornell University) (publishedVersion)
- PubMed (publishedVersion)
- Comillas Repository (Comillas Pontifical University) (publishedVersion, cc-by-nc-nd)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- U.S. Department of Energy (AC05-00OR22725, DE--AC05--00OR22725, DE-AC05?, Contract DE-AC05-00OR22725, 00OR22725)
- National Aeronautics and Space Administration (DE-AC05-00OR22725)
- Office of Science (DEAC05- 00OR22725, AC05-00OR22725)
- Ames Research Center
- University of California, Santa Barbara
- Air Force Research Laboratory (F4HBKC4162G001, DE-AC05-00OR22725)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum advantage, superconducting qubits, classical simulation of quantum circuits, programmable quantum processors, quantum computing
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