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 uniform and the measured XEB will be 0.
“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]
In “A suitable computational task” (page unavailable), the paper states that sampling from a uniform distribution produces an XEB fidelity of 0. It does not explicitly state that overwhelming circuit noise makes the measured distribution uniform, though it links errors to lower XEB fidelity. (LLM, conf=0.81)
Experimental — accuracy not yet measuredGoogle's superconducting processor used for quantum supremacy had single-qubit and two-qubit gate errors of 0.1% and 0.3%, respectively.
“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]
The 'Building a high-fidelity processor' section benchmarks both gate types, but reports an average two-qubit error of 0.36% in isolation and 0.62% when gates run simultaneously—not the claimed 0.3%. The accessible text describes single-qubit errors without giving the claimed 0.1% value; the Fig. 2 caption refers to mean values shown in the figure but does not state them in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredRecent advances in control and readout of quantum bits have underscored the promise of QIP.
“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]
Quantum advantage was demonstrated against the best classical computers for the task of sampling the output of a pseudo-random quantum circuit.
“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
Using 3D [[8,3,2]] code blocks, the authors realize computationally complex sampling circuits with up to 48 logical qubits entangled with hypercube connectivity.
“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]
The authors 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 complexity, showing the benefits of a logical encoding for this application.
“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]
The Sycamore processor takes about 200 s to compute a task that would take approximately 10,000 years by a state-of-the-art classical supercomputer.
“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]
In 2019, researchers executed quantum supremacy successfully in a 53 qubit superconducting machine.
“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]
RCS is a system-level benchmark assessing how effectively a quantum computer can generate computationally complex quantum states.
“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]
The quality of samples can be judged by statistical tests, with the linear cross-entropy test being a widely used standard.
“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]
Existing quantum devices contain on the order of 100 physical qubits.
“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
Quantum computing can provide substantial processing advantages over traditional computing.
“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]
Error correction requires thousands of physical qubits for each logical qubit, resulting in millions of qubits for calculating the FeMoco ground state energy.
“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]
Many NISQ architectures-most notably superconducting qubit devices-can easily and rapidly repeat executions of a quantum circuit.
“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]
The Sycamore quantum computer executed one instance of a quantum circuit a million times in 200 s.
“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]
Google reported fidelities of 0.1% for single-qubit gates and 0.3% for two-qubit gates in a superconducting processor.
“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]
The current record for a controllable, programmable quantum processor is 53 qubits.
“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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The potential and global outlook of integrated photonics for quantum technologies
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Quantum sensors for biomedical applications
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The Evolution of Quantum Key Distribution Networks: On the Road to the Qinternet
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Superconducting qubit to optical photon transduction
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t|ket⟩: a retargetable compiler for NISQ devices
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Variational Quantum Circuits for Deep Reinforcement Learning
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Dynamical Purification Phase Transition Induced by Quantum Measurements
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High-threshold and low-overhead fault-tolerant quantum memory
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“This new gate is also similar to the gates used by Google Quantum AI 57 , which have shown that a square lattice arrangement is possible.”
MethodTransfer learning in hybrid classical-quantum neural networks
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“32(1), which is significantly larger than the statistical fluctuation, thus successfully distinguishing the data of the 30 different groups.”
BackgroundA four-qubit germanium quantum processor
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Controlling quantum many-body dynamics in driven Rydberg atom arraysPreprint
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Intelligent Computing: The Latest Advances, Challenges, and Future
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Transitioning organizations to post-quantum cryptography
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Information scrambling in quantum circuits
Mi, X., Roushan, P., Quintana, C., Mandrà, S., Marshall, J., Neill, C., Arute, F., Arya, K., Atalaya, J., Babbush, R., et al. (2021) · Science · doi:10.1126/science.abg5029 · Cited by 283 · see citing works
Self-citationRealization of High-Fidelity CZ and Z Z -Free iSWAP Gates with a Tunable Coupler
Sung, Y., Ding, L., Braumüller, J., Vepsäläinen, A. P., Kannan, B., Kjærgaard, M., Greene, A., Samach, G., McNally, C., Kim, D. K., et al. (2021) · Physical Review X · doi:10.1103/physrevx.11.021058 · Cited by 279 · see citing works
Hybrid integration methods for on-chip quantum photonics
Kim, J.-H., Aghaeimeibodi, S., Carolan, J., Englund, D., & Waks, E. (2020) · Optica · doi:10.1364/optica.384118 · Cited by 272 · see citing works
- 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 · 2018: 1
- 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 › Computer Science › Artificial Intelligence › 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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