The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
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.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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 cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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
The cited source says what the claim attributes to it.
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Algorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713
Simulating physics with computers
Feynman, R. P. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf02650179 · Cited by 7,691
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552
Self-citationScheme for reducing decoherence in quantum computer memory
Shor, P. W. (1995) · Physical Review A · doi:10.1103/physreva.52.r2493 · Cited by 4,479
Quantum Mechanics Helps in Searching for a Needle in a Haystack
Grover, L. K. (1997) · Physical Review Letters · doi:10.1103/physrevlett.79.325 · Cited by 4,306
Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics
Wallraff, A., Schuster, D. I., Blais, A., Frunzio, L., Huang, R. S., Majer, J., Kumar, S., Girvin, S. M., & Schoelkopf, R. J. (2004) · Nature · doi:10.1038/nature02851 · Cited by 3,796
Charge-insensitive qubit design derived from the Cooper pair box
Koch, J., Yu, T. M., Gambetta, J., Houck, A. A., Schuster, D. I., Majer, J., Blais, A., Devoret, M. H., Girvin, S. M., & Schoelkopf, R. J. (2007) · Physical Review A · doi:10.1103/physreva.76.042319 · Cited by 3,351
Surface codes: Towards practical large-scale quantum computation
Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012) · Physical Review A · doi:10.1103/physreva.86.032324 · Cited by 3,059
Self-citationBarren plateaus in quantum neural network training landscapes
McClean, J. R., Boixo, S., Smelyanskiy, V. N., Babbush, R., & Neven, H. (2018) · Nature Communications · doi:10.1038/s41467-018-07090-4 · Cited by 2,329
Self-citation
Research works printed but not linked: 3
- Farhi, E. & Neven, H. Classification with quantum neural networks on near term processors. Preprint at https://arxiv.org/abs/1802.06002 (2018). · arXiv
- Cross, A. W., Magesan, E., Bishop, L. S., Smolin, J. A. & Gambetta, J. M. Scalable randomised benchmarking of non-Clifford gates. npj Quant. Inform. 2, 16012 (2016).
- Villalonga, B. et al. A flexible high-performance simulator for the verification and benchmarking of quantum circuits implemented on real hardware. npj Quant. Inform. (in the press); preprint at https://arxiv.org/abs/1811.09599 (2018). · arXiv
Cited by
Sorted by citations
Items 601–700 of 1,052
Quantum SMEFT tomography: Top quark pair production at the LHC
Aoude, R., Madge, E., Fabio Maltoni, B., & Mantani, L. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.055007 · Cited by 81
The Transmon Qubit for Electromagnetics Engineers: An introduction
Roth, T. E., Ma, R., & Chew, W. C. (2022) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2022.3176593 · Cited by 77
Annealing-Based Quantum Computing for Combinatorial Optimal Power Flow
Morstyn, T. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3200590 · Cited by 87
A Comprehensive Review of Denial of Service Attacks in Blockchain Ecosystem and Open Challenges
Chaganti, R., Boppana, R. V., Ravi, V., Munir, K., Almutairi, M. S., Rustam, F., Lee, E., & Ashraf, I. (2022) · IEEE Access · doi:10.1109/access.2022.3205019 · Cited by 74
Classical algorithms for quantum mean values
Bravyi, S., Gosset, D., & Movassagh, R. (2021) · Nature Physics · doi:10.1038/s41567-020-01109-8 · Cited by 89
Prospects of quantum computing for molecular sciences
Liu, H., Low, G. H., Steiger, D. S., Häner, T., Reiher, M., & Troyer, M. (2022) · Materials Theory · doi:10.1186/s41313-021-00039-z · Cited by 73
Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
Zhao, L., Goings, J., Shin, K., Kyoung, W., Fuks, J. I., Kevin Rhee, J.-K., Rhee, Y. M., Wright, K., Nguyen, J., Kim, J., et al. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00730-8 · Cited by 73
Tkachenko, N. V., Sud, J., Zhang, Y., Tretiak, S., Anisimov, P. M., Arrasmith, A. T., Coles, P. J., Cincio, L., & Dub, P. A. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020337 · Cited by 73
Predicting new superconductors and their critical temperatures using machine learning
Roter, B., & Dordevic, S. V. (2020) · Physica C Superconductivity · doi:10.1016/j.physc.2020.1353689 · Cited by 73
Bharti, K., & Haug, T. (2021) · Physical Review A · doi:10.1103/physreva.104.042418 · Cited by 79
Xu, Y., Huang, G., Balewski, J. T., Naik, R., Morvan, A., Mitchell, B., Nowrouzi, K., Santiago, D., & Siddiqi, I. A. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3116540 · Cited by 84
Quantum technologies in the telecommunications industry
Martin, V., Brito, J. P., Escribano, C., Menchetti, M., White, C., Lord, A., Wissel, F., Gunkel, M., Gavignet, P., Genay, N., et al. (2021) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-021-00108-9 · Cited by 77
Computational spectroscopy of complex systems
Jansen, T. L. C. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0064092 · Cited by 74
Low-depth quantum state preparation
Zhang, X., Yung, M., & Yuan, X. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043200 · Cited by 84
Long-Distance Transmon Coupler with cz-Gate Fidelity above 99.8 %
Marxer, F., Vepsäläinen, A. P., Jolin, S. W., Tuorila, J., Landra, A., Ockeloen-Korppi, C. F., Liu, W., Ahonen, O., Auer, A., Belzane, L., et al. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.010314 · Cited by 82
Observation of Josephson harmonics in tunnel junctions
Willsch, D., Rieger, D., Winkel, P., Willsch, M., Dickel, C., Krause, J., Ando, Y., Lescanne, R., Leghtas, Z., Bronn, N. T., et al. (2024) · Nature Physics · doi:10.1038/s41567-024-02400-8 · Cited by 73
Self-citationDual-rail encoding with superconducting cavities
Teoh, J. D., Winkel, P., Babla, H. K., Chapman, B. J., Claes, J., de Graaf, S. J., Garmon, J. W. O., Kalfus, W. D., Lu, Y., Maiti, A., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2221736120 · Cited by 97
Optomechanical Ground-State Cooling in a Continuous and Efficient Electro-Optic Transducer
Brubaker, B., Kindem, J. M., Urmey, M. D., Mittal, S., Delaney, R. D., Burns, P. S., Vissers, M. R., Lehnert, K. W., & Regal, C. A. (2022) · Physical Review X · doi:10.1103/physrevx.12.021062 · Cited by 73
Experimental quantum computational chemistry with optimized unitary coupled cluster ansatz
Guo, S., Sun, J., Qian, H., Gong, M., Zhang, Y., Chen, F., Ye, Y., Wu, Y., Cao, S., Liu, K., et al. (2024) · Nature Physics · doi:10.1038/s41567-024-02530-z · Cited by 95
Efficient Quantum Algorithms for Stabilizer Entropies
Haug, T., Lee, S., & Kim, M. S. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.240602 · Cited by 97
Scalable Privacy-Preserving Distributed Learning
Froelicher, D., Troncoso-Pastoriza, J. R., Pyrgelis, A., Sav, S., Sousa, J. S., Bossuat, J.-P., & Hubaux, J. (2021) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2021-0030 · Cited by 82
Anomaly detection with variational quantum generative adversarial networks
Herr, D., Obert, B., & Rosenkranz, M. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac0d4d · Cited by 83
Circuit Compilation Methodologies for Quantum Approximate Optimization Algorithm
Alam, M., Saki, A. A., & Ghosh, S. (2020) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro50266.2020.00029 · Cited by 78
Bidirectional electro-optic wavelength conversion in the quantum ground state
Hease, W., Sanchez, A. R. R., Sahu, R., Wulf, M., Arnold, G., Schwefel, H. G. L., & Fink, J. M. (2020) · Institute of Science and Technology Austria · Cited by 73
Microwave Package Design for Superconducting Quantum Processors
Huang, S., Lienhard, B., Calusine, G., Vepsäläinen, A. P., Braumüller, J., Kim, D. K., Melville, A. J., Niedzielski, B. M., Yoder, J., Kannan, B., et al. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020306 · Cited by 71
Solvable model of deep thermalization with distinct design times
Ippoliti, M., & Ho, W. W. (2022) · Quantum · doi:10.22331/q-2022-12-29-886 · Cited by 89
Low charge noise quantum dots with industrial CMOS manufacturing
Elsayed, A., Shehata, M. M. E. K., Godfrin, C., Kubicek, S., Massar, S., Canvel, Y., Jussot, J., Simion, G., Mongillo, M., Wan, D., et al. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00864-3 · Cited by 71
Benchmarking quantum computers
Proctor, T. J., Young, K. C., Baczewski, A., & Blume-Kohout, R. (2025) · Nature Reviews Physics · doi:10.1038/s42254-024-00796-z · Cited by 116
Random unitaries in extremely low depth
Schuster, T., Haferkamp, J., & Huang, H.-Y. (2025) · Science · doi:10.1126/science.adv8590 · Cited by 181
Derian, J. D., & Wendt, A. E. (2020) · Security Dialogue · doi:10.1177/0967010620901905 · Cited by 75
Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers
Pozzi, M. G., Herbert, S., Sengupta, A., & Mullins, R. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3520434 · Cited by 97
A hybrid classical-quantum approach for multi-class classification
Chalumuri, A., Kune, R., & Manoj, B. S. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03029-9 · Cited by 77
Accurately computing the electronic properties of a quantum ring
Neill, C., McCourt, T., Mi, X., Jiang, Z., Niu, M. Y., Mruczkiewicz, W., Aleiner, I., Arute, F., Arya, K., Atalaya, J., et al. (2021) · Nature · doi:10.1038/s41586-021-03576-2 · Cited by 86
Self-citationIoT Security Challenges: Cloud and Blockchain, Postquantum Cryptography, and Evolutionary Techniques
Balogh, Š., Gallo, O., Ploszek, R., Špaček, P., & Zajac, P. (2021) · Electronics · doi:10.3390/electronics10212647 · Cited by 77
Entanglement Purification and Protection in a Superconducting Quantum Network
Yan, H., Zhong, Y., Chang, H.-S., Bienfait, A., Chou, M.-H., Conner, C. R., Dumur, É., Grebel, J., Povey, R. G., & Cleland, A. N. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.080504 · Cited by 79
Engineering superconducting qubits to reduce quasiparticles and charge noise
Pan, X., Zhou, Y., Yuan, H., Nie, L., Wei, W., Zhang, L., Li, J., Liu, S., Jiang, Z. H., Catelani, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34727-2 · Cited by 70
Self-citationA Comparison of Security and its Performance for Key Agreements in Post-Quantum Cryptography
Borges, F., Reis, P. R., & Pereira, D. A. B. (2020) · IEEE Access · doi:10.1109/access.2020.3013250 · Cited by 76
Development of Quantum Interconnects (QuICs) for Next-Generation Information Technologies
Awschalom, D., Berggren, K. K., Bernien, H., Bhave, S. A., Carr, L. D., Davids, P., Economou, S. E., Englund, D. R., Faraon, A., Fejer, M. M., et al. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.6082/33e5f-d1j97 · Cited by 83
A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
Uthamacumaran, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100226 · Cited by 67
Park, J.-S., Subramanian, S., Lampert, L. F., Mladenov, T., Klotchkov, I., Kurian, D. J., Juarez-Hernandez, E., Esparza, B. P., Kale, S. R., T., A. B. K., et al. (2021) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2021.3115988 · Cited by 71
QuGAN: A Quantum State Fidelity based Generative Adversarial Network
Stein, S. A., Baheri, B., Chen, D., Mao, Y., Guan, Q., Li, A., Fang, B., & Xu, S. (2021) · 2021 IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce52317.2021.00023 · Cited by 69
Silicon photonic devices for scalable quantum information applications
Feng, L., Zhang, M., Wang, J., Zhou, X., Qiang, X., Guo, G., & Ren, X. (2022) · Photonics Research · doi:10.1364/prj.464808 · Cited by 72
Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing
Heurtel, N., Fyrillas, A., de Gliniasty, G., Bihan, R. L., Malherbe, S., Pailhas, M., Bertasi, E., Bourdoncle, B., Emeriau, P.-E., Mezher, R., et al. (2023) · Quantum · doi:10.22331/q-2023-02-21-931 · Cited by 94
Barren plateaus in quantum tensor network optimization
Cervero-Martín, E., Plekhanov, K., & Lubasch, M. (2023) · Quantum · doi:10.22331/q-2023-04-13-974 · Cited by 83
A Polynomial-Time Classical Algorithm for Noisy Random Circuit Sampling
Aharonov, D., Gao, X., Landau, Z. A., Liu, Y., & Vazirani, U. V. (2023) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3564246.3585234 · Cited by 126
Machine learning for anomaly detection in particle physics
Belis, V., Odagiu, P., & Aarrestad, T. K. (2024) · Reviews in Physics · doi:10.1016/j.revip.2024.100091 · Cited by 111
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future TrendsPreprint
Latif, S., Rana, R. K., Khalifa, S., Jurdak, R., Qadir, J., & Schuller, B. W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.00378 · Cited by 108
Quantum circuit optimization with deep reinforcement learningPreprint
Fösel, T., Niu, M. Y., Marquardt, F., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07585 · Cited by 218
Self-citationScalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits
Proctor, T., Seritan, S., Rudinger, K., Nielsen, E., Blume-Kohout, R., & Young, K. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.150502 · Cited by 68
Kerenidis, I., & Landman, J. (2021) · Physical Review A · doi:10.1103/physreva.103.042415 · Cited by 70
Improved Thermal Area Law and Quasilinear Time Algorithm for Quantum Gibbs States
Kuwahara, T., Alhambra, A. M., & Anshu, A. (2021) · Physical Review X · doi:10.1103/physrevx.11.011047 · Cited by 81
Moiré and beyond in transition metal dichalcogenide twisted bilayers
Tran, K., Choi, J., & Singh, A. (2020) · 2D Materials · doi:10.1088/2053-1583/abd3e7 · Cited by 68
Fast Logic with Slow Qubits: Microwave-Activated Controlled-Z Gate on Low-Frequency Fluxoniums
Ficheux, Q., Nguyen, L. B., Somoroff, A., Xiong, H., Nesterov, K., Vavilov, M., & Manucharyan, V. (2021) · Physical Review X · doi:10.1103/physrevx.11.021026 · Cited by 73
Learning temporal data with a variational quantum recurrent neural network
Takaki, Y., Mitarai, K., Negoro, M., Fujii, K., & Kitagawa, M. (2021) · Physical Review A · doi:10.1103/physreva.103.052414 · Cited by 88
Computable and Operationally Meaningful Multipartite Entanglement Measures
Beckey, J. L., Gigena, N., Coles, P. J., & Cerezo, M. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.140501 · Cited by 75
Quantum Neuromorphic Computing with Reservoir Computing Networks
Ghosh, S., Nakajima, K., Krisnanda, T., Fujii, K., & Liew, T. C. H. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202100053 · Cited by 76
Characterization and Modeling of Self-Heating in Nanometer Bulk-CMOS at Cryogenic Temperatures
Hart, P. A. T., Babaie, M., Vladimirescu, A., & Sebastiano, F. (2021) · IEEE Journal of the Electron Devices Society · doi:10.1109/jeds.2021.3116975 · Cited by 68
Universal Fidelity Reduction of Quantum Operations from Weak Dissipation
Abad, T., Fernández-Pendás, J., Kockum, A. F., & Johansson, G. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.150504 · Cited by 78
Near-term quantum algorithms for linear systems of equations with regression loss functions
Huang, H.-Y., Bharti, K., & Rebentrost, P. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac325f · Cited by 193
Wu, H., Cui, Y., Xu, J., Yan, Z., Xie, Z., Hu, Y., & Zhu, S. N. (2022) · Nano Letters · doi:10.1021/acs.nanolett.1c04737 · Cited by 69
Long-lived phantom helix states in Heisenberg quantum magnets
Jepsen, N., Lee, Y. K., Lin, H., Dimitrova, I., Margalit, Y., Ho, W. W., & Ketterle, W. (2022) · Nature Physics · doi:10.1038/s41567-022-01651-7 · Cited by 76
Konar, D., Sarma, A. D., Bhandary, S., Bhattacharyya, S., Cangi, A., & Aggarwal, V. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110099 · Cited by 73
Shi, Y., Yang, R.-Q., Xiang, Z., Ge, Z.-Y., Li, H., Wang, Y.-Y., Huang, K., Tian, Y., Song, X., Zheng, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39064-6 · Cited by 82
Transforming research with quantum computing
Gill, S. S., & Buyya, R. (2024) · Journal of Economy and Technology · doi:10.1016/j.ject.2024.07.001 · Cited by 85
Quantum Optimization for the Graph Coloring Problem with Space-Efficient Embedding
Tabi, Z., El-Safty, K. H., Kallus, Z., Haga, P., Kozsik, T., Glos, A., & Zimborás, Z. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00018 · Cited by 85
A hybrid quantum computing pipeline for real world drug discovery
Li, W., Yin, Z., Li, X., Ma, D., Yi, S., Zhang, Z., Zou, C., Bu, K., Dai, M., Yue, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-67897-8 · Cited by 67
Self-citationDesigning Peptides on a Quantum Computer
Mulligan, V. K., Melo, H., Merritt, H. I., Slocum, S., Weitzner, B. D., Watkins, A. M., Renfrew, P. D., Pelissier, C., Arora, P. S., & Bonneau, R. (2019) · doi:10.1101/752485 · Cited by 67
Direct measurement of nonlocal interactions in the many-body localized phase
Chiaro, B., Neill, C., Bohrdt, A., Filippone, M., Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., et al. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013148 · Cited by 87
Self-citationHan, X., Cai, T., Li, X., Wu, Y., W., Y., Ma, Y. L., Wang, J., Zhang, H. Y., Song, Y., & Duan, L.-M. (2020) · Physical Review A · doi:10.1103/physreva.102.022619 · Cited by 67
Adaptive pruning-based optimization of parameterized quantum circuits
Sim, S., Romero, J., Gonthier, J. F., & Kunitsa, A. A. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abe107 · Cited by 75
Probing Vibrational Symmetry Effects and Nuclear Spin Economy Principles in Molecular Spin Qubits
Santanni, F., Albino, A., Atzori, M., Ranieri, D., Salvadori, E., Chiesa, M., Lunghi, A., Bencini, A., Sorace, L., Totti, F., et al. (2020) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.0c02573 · Cited by 69
High-speed thin-film lithium niobate quantum processor driven by a solid-state quantum emitter
Sund, P. I., Lomonte, E., Paesani, S., Wang, Y., Carolan, J., Bart, N., Wieck, A. D., Ludwig, A., Midolo, L., Pernice, W. H. P., et al. (2023) · Science Advances · doi:10.1126/sciadv.adg7268 · Cited by 76
Hamiltonian variational ansatz without barren plateaus
Park, C.-Y., & Killoran, N. (2024) · Quantum · doi:10.22331/q-2024-02-01-1239 · Cited by 90
Thermalization and criticality on an analogue–digital quantum simulator
Andersen, T. I., Astrakhantsev, N., Karamlou, A. H., Berndtsson, J., Motruk, J., Szasz, A., Gross, J. A., Schuckert, A., Westerhout, T., Zhang, Y., et al. (2025) · Nature · doi:10.1038/s41586-024-08460-3 · Cited by 105
Self-citationQuantum computing for chemical and biomolecular product design
Andersson, M. P., Jones, M. N., Mikkelsen, K. V., You, F., & Mansouri, S. S. (2022) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100754 · Cited by 66
Optimal control of traffic signals using quantum annealing
Hussain, H., Javaid, M. B., Khan, F. S., Dalal, A., & Khalique, A. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02815-1 · Cited by 66
Ayral, T., Louvet, T., Zhou, Y., Lambert, C., Stoudenmire, E. M., & Waintal, X. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.020304 · Cited by 66
Fujii, K., Mizuta, K., Ueda, H., Mitarai, K., Mizukami, W., & Nakagawa, Y. O. (2020) · arXiv · doi:10.48550/arxiv.2007.10917 · Cited by 66
Methods for Measuring Magnetic Flux Crosstalk between Tunable Transmons
Abrams, D. M., Didier, N., Caldwell, S. A., Johnson, B. R., & Ryan, C. A. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.12.064022 · Cited by 66
Fujii, K., Mizuta, K., Ueda, H., Mitarai, K., Mizukami, W., & Nakagawa, Y. O. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.010346 · Cited by 83
Fault-Tolerant Parity Readout on a Shuttling-Based Trapped-Ion Quantum Computer
Hilder, J., Pijn, D. R. M., Onishchenko, O., Stahl, A., Orth, M., Lekitsch, B., Blanco, A., Müller, M., Schmidt‐Kaler, F., & Poschinger, U. (2022) · Physical Review X · doi:10.1103/physrevx.12.011032 · Cited by 73
Nonreciprocal single-photon quantum router
Ren, Y.-L., Ma, S.-L., Xie, J.-K., Li, X., Cao, M., & Li, F. (2022) · Physical Review A · doi:10.1103/physreva.105.013711 · Cited by 66
Importance of kernel bandwidth in quantum machine learning
Shaydulin, R., & Wild, S. M. (2022) · Physical Review A · doi:10.1103/physreva.106.042407 · Cited by 71
Resources for Bosonic Quantum Computational Advantage
Chabaud, U., & Walschaers, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.090602 · Cited by 80
Hybrid quantum-classical reservoir computing of thermal convection flow
Pfeffer, P., Heyder, F., & Schumacher, J. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033176 · Cited by 78
The Future of Cybersecurity in the Age of Quantum Computers
Raheman, F. (2022) · Future Internet · doi:10.3390/fi14110335 · Cited by 66
Experimental Evaluation of Quantum Machine Learning Algorithms
Simoes, R. D. M., Huber, P., Meier, N., Smailov, N., Füchslin, R. M., & Stockinger, K. (2023) · IEEE Access · doi:10.1109/access.2023.3236409 · Cited by 74
Digitized Counterdiabatic Quantum Algorithm for Protein Folding
Chandarana, P., Hegade, N. N., Montalban, I., Solano, E., & Chen, X. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.014024 · Cited by 68
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
Park, S., Kim, J. P., Park, C., Jung, S., & Kim, J. (2023) · IEEE Communications Magazine · doi:10.1109/mcom.020.2300199 · Cited by 66
Stable organic radical qubits and their applications in quantum information science
Zhou, A.-M., Sun, Z., & Sun, L. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100662 · Cited by 71
Phase transition in magic with random quantum circuits
Niroula, P., White, C. D., Wang, Q., Johri, S., Zhu, D., Monroe, C. R., Noel, C., & Gullans, M. J. (2024) · Nature Physics · doi:10.1038/s41567-024-02637-3 · Cited by 66
Devadas, R. M., & Sowmya, T. (2025) · MethodsX · doi:10.1016/j.mex.2025.103318 · Cited by 90
High coherence superconducting microwave cavities with indium bump bonding
Lei, C. U., Krayzman, L., Ganjam, S., Frunzio, L., & Schoelkopf, R. J. (2020) · Applied Physics Letters · doi:10.1063/5.0003907 · Cited by 65
Many-Body Chern Number from Statistical Correlations of Randomized Measurements
Cian, Z.-P., Dehghani, H., Elben, A., Vermersch, B., Zhu, G., Barkeshli, M., Zoller, P., & Hafezi, M. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.050501 · Cited by 69
Zhao, R., Park, S., Zhao, T., Bal, M., McRae, C. R. H., Long, J., & Pappas, D. P. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.14.064006 · Cited by 65
A single inverse-designed photonic structure that performs parallel computing
Camacho, M., Edwards, B. E., & Engheta, N. (2021) · Nature Communications · doi:10.1038/s41467-021-21664-9 · Cited by 74
Theoretical Prediction of Two-Dimensional Materials, Behavior, and Properties
Penev, E. S., Marzari, N., & Yakobson, B. I. (2021) · ACS Nano · doi:10.1021/acsnano.0c10504 · Cited by 65
Carbon Nanotube Devices for Quantum Technology
Baydin, A., Tay, F., Fan, J., Manjappa, M., Gao, W., & Kono, J. (2022) · Materials · doi:10.3390/ma15041535 · Cited by 67
Software engineering for quantum programming: How far are we?
Stefano, M. D., Pecorelli, F., Nucci, D. D., Palomba, F., & Lucia, A. D. (2022) · Journal of Systems and Software · doi:10.1016/j.jss.2022.111326 · Cited by 81
Space-efficient binary optimization for variational quantum computing
Glos, A., Krawiec, A., & Zimborás, Z. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00546-y · Cited by 103
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
