The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
The theory of variational hybrid quantum-classical algorithms
Open accessAbstract
Many quantum algorithms have daunting resource requirements when compared to what is available today. To address this discrepancy, a quantum-classical hybrid optimization scheme known as 'the quantum variational eigensolver' was developed (Peruzzo et al 2014 Nat. Commun. 5 4213 ) with the philosophy that even minimal quantum resources could be made useful when used in conjunction with classical routines. In this work we extend the general theory of this algorithm and suggest algorithmic improvements for practical implementations. Specifically, we develop a variational adiabatic ansatz and explore unitary coupled cluster where we establish a connection from second order unitary coupled cluster to universal gate sets through a relaxation of exponential operator splitting. We introduce the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this procedure. Finally, we show how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.
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
“To this end, it has been shown that by considering general commutations (and increasing the number of gates of the circuit quadratically with n ) the scaling of the number of measurements can be reduced to n 3 [213, 214, 216-219].”
From Variational quantum algorithms (2021) · cites this paper as [218]
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
“Motivated by adiabatic state preparation, the Variational Hamiltonian Ansatz (VHA) was developed to reduce the number of parameters and accelerate the convergence (McClean et al. , 2016; Wecker et al. , 2015).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as McClean et al., 2016
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
“The electronic structure problem is one of the most prominent tasks within VQAs (see for example the reviews (Cao et al. , 2019; McArdle et al. , 2020)) and was the pioneering task for VQE (McClean et al. , 2016; Peruzzo et al. , 2014).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as McClean et al. , 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“An important class of algorithms for NISQ hardware are socalled hybrid quantum-classical algorithms that partition the computational workload between a classical and a quantum computer.[124,125]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [124]
The cited source says what the claim attributes to it.
In the citing paper
“Pauli strings that commute qubit-wise, i.e. the Pauli operators on each qubit commute, can be measured at the same time (Kandala et al. , 2017; McClean et al. , 2016).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as McClean et al., 2016
The cited source says what the claim attributes to it.
In the citing paper
“To this end, VQE (McClean et al. , 2016; Peruzzo et al. , 2014) has been proposed, to find the ground state of a Hamiltonian H in a manner that is suited for NISQ devices (Wecker et al. , 2015).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as McClean et al. , 2016
In the citing paper
“In contrast, the Boson Sampling model of computation does not have this property 63 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [63]
In the citing paper
“Many algorithms for near-term quantum computers are based on variational circuits [1-6]”
From Estimating the gradient and higher-order derivatives on quantum hardware (2021) · cites this paper as [4]
In the citing paper
“This definition differs from [13, 14] and loosely coincides with the complexity of classically simulating the model circuits.”
From Validating quantum computers using randomized model circuits (2019) · cites this paper as [14]
In the citing paper
“Hybrid quantum-classical algorithms, such as the variational quantum eigensolver 2,3 (VQE) and the quantum approximate optimization algorithm 4,5 (QAOA), provide ways to use NISQ computers for practical applications.”
From A generative modeling approach for benchmarking and training shallow quantum circuits (2019) · cites this paper as [3]
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 Computation and Quantum Information
Nielsen, M. A., Chuang, I., & Grover, L. K. (2002) · American Journal of Physics · doi:10.1119/1.1463744 · Cited by 21,895
Braun, W. J., & Murdoch, D. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993456.011 · Cited by 14,099
(1999) · Cited by 12,854
Nocedal, J. M., & Wright, S. J. (2006) · Springer series in operations research/Springer series in operations research and financial engineering · doi:10.1007/978-0-387-40065-5 · Cited by 10,206
A fifth-order perturbation comparison of electron correlation theories
Raghavachari, K., Trucks, G. W., Pople, J. A., & Head‐Gordon, M. (1989) · Chemical Physics Letters · doi:10.1016/s0009-2614(89)87395-6 · Cited by 8,511
Quantum Computation and Quantum Information: 10th Anniversary Edition
Nielsen, M. A., & Chuang, I. L. (2012) · doi:10.1017/cbo9780511976667 · Cited by 8,436
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-citationThe Mathematical Theory of Optimal Processes
Bellman, R., Pontryagin, L. S., Boltyanskii, V. G., Gamkrelidze, R. V., Mishchenko, E. F., Trirogoff, Κ. N., & Neustadt, L. W. (1965) · Econometrica · doi:10.2307/1911909 · Cited by 4,212
Coupled-cluster theory in quantum chemistry
Bartlett, R. J., & Musiał, M. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.291 · Cited by 3,343
Research works printed but not linked: 4
- Bravyi S, 2000, Ann. Phys., 298, 18
- Urban M, 1985, Chem. Phys., 83, 4041
- 10.1002/9783527626212 · DOI
- Holmström K, 2015, User’s Guide for TOMLAB
Cited by
Sorted by citations
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
“y states of many-particle quantum systems (large molecules, for example). When applied to quantum problems this hybrid quantum-classical procedure goes by the name Variation Quantum Eigensolver (VQE) [23]. Will NISQ technology running QAOA or VQE be able to outperform classical algorithms that find approximate solution…”
MethodVariational quantum algorithms
Cerezo, M., Arrasmith, A., Babbush, R., Benjamin, S. C., Endo, S., Fujii, K., McClean, J. R., Mitarai, K., Yuan, X., Cincio, L., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00348-9 · Cited by 3,385
“Indeed, this was the first method introduced [192].”
MethodSelf-citationHardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M. (2017) · Nature · doi:10.1038/nature23879 · Cited by 2,982
Machine learning and the physical sciences
Carleo, G., Cirac, J. I., Cranmer, K. S., Daudet, L., Schuld, M., Tishby, N., Vogt-Maranto, L., & Zdeborová, L. (2019) · Reviews of Modern Physics · doi:10.1103/revmodphys.91.045002 · Cited by 2,640
Barren 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-citationQuantum Machine Learning in Feature Hilbert Spaces
Schuld, M., & Killoran, N. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.040504 · Cited by 1,840
Noisy intermediate-scale quantum algorithms
Bharti, K., Cervera-Lierta, A., Kyaw, T. H., Haug, T., Alperin-Lea, S., Anand, A., Degroote, M., Heimonen, H., Kottmann, J. S., Menke, T., et al. (2022) · Reviews of Modern Physics · doi:10.1103/revmodphys.94.015004 · Cited by 2,231
Self-citationQuantum computational chemistry
McArdle, S., Endo, S., Aspuru-Guzik, A., Benjamin, S. C., & Yuan, X. (2020) · Reviews of Modern Physics · doi:10.1103/revmodphys.92.015003 · Cited by 1,621
Self-citationQuantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., et al. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00803 · Cited by 1,560
Self-citationError Mitigation for Short-Depth Quantum Circuits
Temme, K., Bravyi, S., & Gambetta, J. M. (2017) · Physical Review Letters · doi:10.1103/physrevlett.119.180509 · Cited by 1,379
Quantum Chemistry in the Age of Quantum Computing
Cao, Y., Romero, J., Olson, J., Degroote, M., Johnson, P., Kieferová, M., Kivlichan, I., Menke, T., Peropadre, B., Sawaya, N., et al. (n.d.) · OPUS - Open Publications of UTS Scholars (University of Technology Sydney) · Cited by 1,265
Self-citationMachine learning & artificial intelligence in the quantum domain: a review of recent progress
Dunjko, V., & Briegel, H. J. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aab406 · Cited by 1,241
Cost function dependent barren plateaus in shallow parametrized quantum circuits
Cerezo, M., Sone, A., Volkoff, T., Cincio, L., & Coles, P. J. (2021) · Nature Communications · doi:10.1038/s41467-021-21728-w · Cited by 1,047
Circuit-centric quantum classifiers
Schuld, M., Bocharov, A., Svore, K. M., & Wiebe, N. (2020) · Physical Review A · doi:10.1103/physreva.101.032308 · Cited by 1,057
The Variational Quantum Eigensolver: A review of methods and best practices
Tilly, J., Chen, H., Cao, S., Picozzi, D., Setia, K., Li, Y., Grant, E., Wossnig, L., Rungger, I., Booth, G. H., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.08.003 · Cited by 1,031
An adaptive variational algorithm for exact molecular simulations on a quantum computer
Grimsley, H. R., Economou, S. E., Barnes, E., & Mayhall, N. J. (2019) · Nature Communications · doi:10.1038/s41467-019-10988-2 · Cited by 1,033
Power of data in quantum machine learning
Huang, H.-Y., Broughton, M., Mohseni, M., Babbush, R., Boixo, S., Neven, H., & McClean, J. R. (2021) · Nature Communications · doi:10.1038/s41467-021-22539-9 · Cited by 896
Self-citationSim, S., Johnson, P. D., & Aspuru‐Guzik, A. (2019) · Advanced Quantum Technologies · doi:10.1002/qute.201900070 · Cited by 972
Self-citationError mitigation extends the computational reach of a noisy quantum processor
Kandala, A., Temme, K., Corcoles, A. D., Mezzacapo, A., Chow, J. M., & Gambetta, J. (2019) · Nature · doi:10.1038/s41586-019-1040-7 · Cited by 947
Quantum Algorithms for Quantum Chemistry and Quantum Materials Science
Bauer, B., Bravyi, S., Motta, M., & Chan, G. K.-L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00829 · Cited by 848
Scalable Quantum Simulation of Molecular Energies
O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059
Self-citationProgrammable Quantum Simulations of Spin Systems with Trapped Ions
Monroe, C., Campbell, W. C., Duan, L. M., Gong, Z. X., Gorshkov, A. V., Hess, P., Islam, R., Kim, K., Linke, N., Pagano, G., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.07845 · Cited by 806
Programmable quantum simulations of spin systems with trapped ions
Monroe, C. R., Campbell, W. C., Duan, L.-M., Gong, Z., Gorshkov, A. V., Hess, P., Islam, R., Kim, K., Linke, N. M., Pagano, G., et al. (2021) · Reviews of Modern Physics · doi:10.1103/revmodphys.93.025001 · Cited by 891
Quantum optimization using variational algorithms on near-term quantum devices
Moll, N., Barkoutsos, P., Bishop, L. S., Chow, J. M., Cross, A., Egger, D. J., Filipp, S., Fuhrer, A., Gambetta, J. M., Ganzhorn, M., et al. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aab822 · Cited by 744
Effect of data encoding on the expressive power of variational quantum-machine-learning models
Schuld, M., Sweke, R., & Meyer, J. J. (2021) · Physical Review A · doi:10.1103/physreva.103.032430 · Cited by 739
Noise-induced barren plateaus in variational quantum algorithms
Wang, S., Fontana, E., Cerezo, M., Sharma, K., Sone, A., Cincio, L., & Coles, P. J. (2021) · Nature Communications · doi:10.1038/s41467-021-27045-6 · Cited by 773
Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz
Romero, J., Babbush, R., McClean, J. R., Hempel, C., Love, P. J., & Aspuru-Guzik, A. (2019) · Quantum Science and Technology · doi:10.1088/2058-9565/aad3e4 · Cited by 726
Self-citationMotta, M., Sun, C., Tan, A. T. K., O’Rourke, M. J., Ye, E., Minnich, A. J., Brandão, F. G. S. L., & Chan, G. K.-L. (2020) · Nature Physics · doi:10.1038/s41567-019-0704-4 · Cited by 709
Materials challenges and opportunities for quantum computing hardware
de Leon, N. P., Itoh, K. M., Kim, D., Mehta, K. K., Northup, T. E., Paik, H., Palmer, B. S., Samarth, N., Sangtawesin, S., & Steuerman, D. W. (2021) · Science · doi:10.1126/science.abb2823 · Cited by 710
Validating quantum computers using randomized model circuits
Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D., & Gambetta, J. M. (2019) · Physical Review A · doi:10.1103/physreva.100.032328 · Cited by 704
Efficient Variational Quantum Simulator Incorporating Active Error Minimization
Li, Y., & Benjamin, S. C. (2017) · Physical Review X · doi:10.1103/physrevx.7.021050 · Cited by 686
A Survey on quantum computing technology
Gyongyosi, L., & Imre, S. (2019) · Computer Science Review · doi:10.1016/j.cosrev.2018.11.002 · Cited by 646
Cai, Z., Babbush, R., Benjamin, S. C., Endo, S., Huggins, W. J., Li, Y., McClean, J. R., & O’Brien, T. E. (2023) · Reviews of Modern Physics · doi:10.1103/revmodphys.95.045005 · Cited by 847
Self-citationQuantum Chemistry Calculations on a Trapped-Ion Quantum Simulator
Hempel, C., Maier, C., Romero, J., McClean, J., Monz, T., Shen, H., Jurcevic, P., Lanyon, B. P., Love, P., Babbush, R., et al. (2018) · Physical Review X · doi:10.1103/physrevx.8.031022 · Cited by 629
Self-citationSelf-verifying variational quantum simulation of lattice models
Kokail, C., Maier, C., van Bijnen, R., Brydges, T., Joshi, M. K., Jurcevic, P., Muschik, C. A., Silvi, P., Blatt, R., Roos, C. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1177-4 · Cited by 594
Hybrid Quantum-Classical Algorithms and Quantum Error Mitigation
Endo, S., Cai, Z., Benjamin, S. C., & Yuan, X. (2021) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.90.032001 · Cited by 566
Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus
Holmes, Z., Sharma, K., Cerezo, M., & Coles, P. J. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.010313 · Cited by 570
Practical Quantum Error Mitigation for Near-Future Applications
Endo, S., Benjamin, S. C., & Li, Y. (2018) · Physical Review X · doi:10.1103/physrevx.8.031027 · Cited by 562
Variational ansatz-based quantum simulation of imaginary time evolution
McArdle, S., Jones, T., Endo, S., Li, Y., Benjamin, S. C., & Yuan, X. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0187-2 · Cited by 557
Quantum computing with neutral atoms
Henriet, L., Beguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O., & Jurczak, C. (2020) · Quantum · doi:10.22331/q-2020-09-21-327 · Cited by 550
Demonstration of the trapped-ion quantum CCD computer architecture
Pino, J. M., Dreiling, J. M., Figgatt, C., Gaebler, J. P., Moses, S. A., Allman, M. S., Baldwin, C. H., Foss-Feig, M., Hayes, D., Mayer, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03318-4 · Cited by 547
Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states
McClean, J. R., Kimchi-Schwartz, M. E., Carter, J., & de Jong, W. A. (2017) · Physical Review A · doi:10.1103/physreva.95.042308 · Cited by 516
Self-citationQuantum autoencoders for efficient compression of quantum data
Romero, J., Olson, J. P., & Aspuru-Guzik, A. (2017) · Quantum Science and Technology · doi:10.1088/2058-9565/aa8072 · Cited by 559
Self-citationOpenFermion: the electronic structure package for quantum computers
McClean, J. R., Rubin, N. C., Sung, K. J., Kivlichan, I., Bonet-Monroig, X., Cao, Y., Dai, C., Fried, E., Gidney, C., Gimby, B., et al. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab8ebc · Cited by 621
Self-citationQuantum Generative Adversarial Networks for learning and loading random distributions
Zoufal, C., Lucchi, A., & Woerner, S. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0223-2 · Cited by 480
Variational Quantum Computation of Excited States
Higgott, O., Wang, D., & Brierley, S. (2019) · Quantum · doi:10.22331/q-2019-07-01-156 · Cited by 463
Theory of variational quantum simulation
Yuan, X., Endo, S., Zhao, Q., Li, Y., & Benjamin, S. C. (2019) · Quantum · doi:10.22331/q-2019-10-07-191 · Cited by 458
Variational Quantum Circuits for Deep Reinforcement Learning
Chen, S. Y.-C., Yang, C.-H. H., Qi, J., Chen, P., Ma, X., & Goan, H. (2020) · IEEE Access · doi:10.1109/access.2020.3010470 · Cited by 493
Quantum-classical computation of Schwinger model dynamics using quantum computers
Klco, N., Dumitrescu, E., McCaskey, A., Morris, T., Pooser, R. C., Sanz, M., Solano, E., Lougovski, P., & A, M. J. S. (2018) · Physical Review A · doi:10.1103/physreva.98.032331 · Cited by 428
Transfer learning in hybrid classical-quantum neural networks
Mari, A., Bromley, T. R., Izaac, J., Schuld, M., & Killoran, N. (2020) · Quantum · doi:10.22331/q-2020-10-09-340 · Cited by 435
Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
Kivlichan, I. D., McClean, J., Wiebe, N., Gidney, C., Aspuru-Guzik, A., Chan, G. K.-L., & Babbush, R. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.110501 · Cited by 431
Self-citationGeneralized Unitary Coupled Cluster Wave functions for Quantum Computation
Lee, J., Huggins, W. J., Head-Gordon, M., & Whaley, K. B. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b01004 · Cited by 432
A review on Quantum Approximate Optimization Algorithm and its variants
Blekos, K., Brand, D., Ceschini, A., Chou, C.-H., Li, R.-H., Pandya, K., & Summer, A. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.03.002 · Cited by 563
Tang, H. L., Shkolnikov, V. O., Barron, G. S., Grimsley, H. R., Mayhall, N. J., Barnes, E., & Economou, S. E. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020310 · Cited by 408
Quantum Computing for Finance: State-of-the-Art and Future Prospects
Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., Simonetto, A., Woerner, S., & Yndurain, E. (2020) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2020.3030314 · Cited by 466
Continuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2019) · Physical Review Research · doi:10.1103/physrevresearch.1.033063 · Cited by 418
Cloud Quantum Computing of an Atomic Nucleus
Dumitrescu, E. F., McCaskey, A. J., Hagen, G., Jansen, G. R., Morris, T. D., Papenbrock, T., Pooser, R. C., Dean, D. J., & Lougovski, P. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.210501 · Cited by 393
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
Babbush, R., Gidney, C., Berry, D. W., Wiebe, N., McClean, J., Paler, A., Fowler, A., & Neven, H. (2018) · Physical Review X · doi:10.1103/physrevx.8.041015 · Cited by 383
Self-citationSuperconducting quantum computing: a review
Huang, H.-L., Wu, D., Fan, D., & Zhu, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-020-2881-9 · Cited by 412
A generative modeling approach for benchmarking and training shallow quantum circuits
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., & Perdomo-Ortiz, A. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0157-8 · Cited by 379
Barkoutsos, P. K., Gonthier, J. F., Sokolov, I., Moll, N., Salis, G., Fuhrer, A., Ganzhorn, M., Egger, D. J., Troyer, M., Mezzacapo, A., et al. (2018) · Physical Review A · doi:10.1103/physreva.98.022322 · Cited by 363
Towards quantum machine learning with tensor networks
Huggins, W. J., Patil, P., Mitchell, B., Whaley, K. B., & Stoudenmire, E. M. (2018) · Quantum Science and Technology · doi:10.1088/2058-9565/aaea94 · Cited by 398
Quantum simulation and computing with Rydberg-interacting qubits
Morgado, M., & Whitlock, S. (2021) · AVS Quantum Science · doi:10.1116/5.0036562 · Cited by 370
Computation of Molecular Spectra on a Quantum Processor with an Error-Resilient Algorithm
Colless, J. I., Ramasesh, V. V., Dahlen, D., Blok, M. S., Kimchi-Schwartz, M. E., McClean, J. R., Carter, J., de Jong, W. A., & Siddiqi, I. (2018) · Physical Review X · doi:10.1103/physrevx.8.011021 · Cited by 460
Self-citationQuantum Generative Adversarial Networks for learning and loading random distributions
Zoufal, C., Lucchi, A., & Woerner, S. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000382432 · Cited by 390
Potential of quantum computing for drug discovery
Cao, Y., Romero, J., & Aspuru-Guzik, A. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2018.2888987 · Cited by 357
Self-citationContinuous-variable quantum neural networks
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 358
Low-Depth Quantum Simulation of Materials
Babbush, R., Wiebe, N., McClean, J., McClain, J., Neven, H., & Chan, G. K.-L. (2018) · Physical Review X · doi:10.1103/physrevx.8.011044 · Cited by 335
Self-citationVariational quantum algorithms for nonlinear problems
Lubasch, M., Joo, J., Moinier, P., Kiffner, M., & Jaksch, D. (2020) · Physical Review A · doi:10.1103/physreva.101.010301 · Cited by 332
Strawberry Fields: A Software Platform for Photonic Quantum Computing
Killoran, N., Izaac, J., Quesada, N., Bergholm, V., Amy, M., & Weedbrook, C. (2019) · Quantum · doi:10.22331/q-2019-03-11-129 · Cited by 395
Robust data encodings for quantum classifiers
LaRose, R., & Coyle, B. (2020) · Physical Review A · doi:10.1103/physreva.102.032420 · Cited by 367
Quantum Simulation for High-Energy Physics
Bauer, C., Davoudi, Z., Balantekin, A. B., Tanmoy Bhattacharya, A., Carena, M. S., de Jong, W. A., Draper, P., El-Khadra, A. X., Gemelke, N., Hanada, M., et al. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.027001 · Cited by 503
Layerwise learning for quantum neural networks
Skolik, A., McClean, J. R., Mohseni, M., van der Smagt, P., & Leib, M. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00036-4 · Cited by 324
Self-citationExperimental Quantum Generative Adversarial Networks for Image Generation
Huang, H.-L., Du, Y., Gong, M., Zhao, Y., Wu, Y., Wang, C., Li, S., Liang, F., Lin, J., Xu, Y., et al. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.024051 · Cited by 342
Quantum Computation of Electronic Transitions Using a Variational Quantum Eigensolver
Parrish, R. M., Hohenstein, E. G., McMahon, P. L., & Martínez, T. J. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.230401 · Cited by 282
Hybrid Quantum-Classical Approach to Quantum Optimal Control
Li, J., Yang, X., Peng, X., & Sun, C.-P. (2017) · Physical Review Letters · doi:10.1103/physrevlett.118.150503 · Cited by 278
Simulating Large Quantum Circuits on a Small Quantum Computer
Peng, T., Harrow, A. W., Ozols, M., & Wu, X. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.150504 · Cited by 324
Gard, B. T., Zhu, L., Barron, G. S., Mayhall, N. J., Economou, S. E., & Barnes, E. (2020) · npj Quantum Information · doi:10.1038/s41534-019-0240-1 · Cited by 266
Measurement optimization in the variational quantum eigensolver using a minimum clique cover
Verteletskyi, V., Yen, T.-C., & Izmaylov, A. F. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5141458 · Cited by 269
Hybrid quantum-classical convolutional neural networks
Liu, J., Lim, K. H., Wood, K. L., Huang, W., Guo, C., & Huang, H.-L. (2021) · Science China Physics, Mechanics & Astronomy · doi:10.1007/s11433-021-1734-3 · Cited by 289
Variational quantum algorithms for discovering Hamiltonian spectra
Jones, T., Endo, S., McArdle, S., Yuan, X., & Benjamin, S. C. (2019) · Physical Review A · doi:10.1103/physreva.99.062304 · Cited by 256
Quantum Computer Systems for Scientific Discovery
Alexeev, Y., Bacon, D., Brown, K. R., Calderbank, R., Carr, L. D., Chong, F. T., DeMarco, B., Englund, D. R., Farhi, E., Fefferman, B., et al. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.017001 · Cited by 339
Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers
Huggins, W. J., McClean, J. R., Rubin, N. C., Jiang, Z., Wiebe, N., Whaley, K. B., & Babbush, R. (2021) · npj Quantum Information · doi:10.1038/s41534-020-00341-7 · Cited by 256
Self-citationQuantum Long Short-Term Memory
Chen, S. Y.-C., Yoo, S., & Fang, Y.-L. L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747369 · Cited by 281
A versatile single-photon-based quantum computing platform
Maring, N., Fyrillas, A., Pont, M., Ivanov, E., Stepanov, P., Margaria, N., Hease, W., Pishchagin, A., Lemaître, A., Sagnes, I., et al. (2024) · Nature Photonics · doi:10.1038/s41566-024-01403-4 · Cited by 314
“We train a quantum neural network 48 on Ascella for a supervised learning classification task.”
Low-cost error mitigation by symmetry verification
Bonet-Monroig, X., Sagastizabal, R., Singh, M., & O'Brien, T. E. (2018) · Physical Review A · doi:10.1103/physreva.98.062339 · Cited by 250
Amplitude estimation without phase estimation
Suzuki, Y., Uno, S., Raymond, R., Tanaka, T., Onodera, T., & Yamamoto, N. (2020) · Quantum Information Processing · doi:10.1007/s11128-019-2565-2 · Cited by 249
Error-Mitigated Digital Quantum Simulation
McArdle, S., Yuan, X., & Benjamin, S. C. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.180501 · Cited by 253
Quantum Algorithm Implementations for Beginners
Jayakumar, A., Adedoyin, A. A., Ambrosiano, J. J., Anisimov, P. M., Casper, W., Chennupati, G., Coffrin, C., Djidjev, H. N., Gunter, D., Karra, S., et al. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3517340 · Cited by 362
Quantum Computer Systems for Scientific Discovery
Alexeev, Y., Bacon, D., Brown, K. R., Calderbank, R., Carr, L. D., Chong, F. T., DeMarco, B., Englund, D., Farhi, E., Fefferman, B., et al. (2021) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.6082/h7fqg-6vt65 · Cited by 257
Accelerated Variational Quantum Eigensolver
Wang, D., Higgott, O., & Brierley, S. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.140504 · Cited by 241
Variational Quantum Simulation of General Processes
Endo, S., Sun, J., Li, Y., Benjamin, S. C., & Yuan, X. (2020) · Physical Review Letters · doi:10.1103/physrevlett.125.010501 · Cited by 237
A Practical Quantum Instruction Set ArchitecturePreprint
Smith, R. S., Curtis, M. J., & Zeng, W. J. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.fsttcs.2021.51 · Cited by 315
Variational fast forwarding for quantum simulation beyond the coherence time
Cîrstoiu, C., Holmes, Z., Iosue, J., Cincio, L., Coles, P. J., & Sornborger, A. (2020) · npj Quantum Information · doi:10.1038/s41534-020-00302-0 · Cited by 232
“In order to approach the issue of initializing the parameters θ and γ, we often use a perturbative method [15, 36] in which we pre-train these parameters for a slightly different Hamiltonian.”
MethodProvably efficient machine learning for quantum many-body problems
Huang, H.-Y., Kueng, R., Torlai, G., Albert, V. V., & Preskill, J. P. (2022) · Science · doi:10.1126/science.abk3333 · Cited by 312
Simulating the vibrational quantum dynamics of molecules using photonics
Sparrow, C., Martín-López, E., Maraviglia, N., Neville, A., Harrold, C., Carolan, J., Joglekar, Y. N., Hashimoto, T., Matsuda, N., O’Brien, J. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0152-9 · Cited by 230
Exact parameterization of fermionic wave functions via unitary coupled cluster theory
Evangelista, F. A., Chan, G. K.-L., & Scuseria, G. E. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5133059 · Cited by 225
A quantum deep convolutional neural network for image recognition
Li, Y., Zhou, R.-G., Xu, R., Luo, J., & Hu, W. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab9f93 · Cited by 225
VQE method: a short survey and recent developments
Fedorov, D. A., Peng, B., Govind, N., & Alexeev, Y. (2022) · Materials Theory · doi:10.1186/s41313-021-00032-6 · Cited by 224
A quantum computing view on unitary coupled cluster theory
Anand, A., Schleich, P., Alperin-Lea, S., Jensen, P. W. K., Sim, S., Díaz‐Tinoco, M., Kottmann, J. S., Degroote, M., Izmaylov, A. F., & Aspuru‐Guzik, A. (2022) · Chemical Society Reviews · doi:10.1039/d1cs00932j · Cited by 251
Self-citation
Metadata
- Authors
- McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Harvard University (US)
- Google (United States) (US)
- Corresponding author
- McClean, Jarrod R
- Venue
- New Journal of Physics
- Publisher
- IOP Publishing
- Published
- 2016-02-04
- Volume
- 18
- Issue
- 2
- Pages
- 023023
- ISSN
- 1367-2630 (ISSN-L)
- OpenAlex
- W2257937122
- Semantic Scholar
- c78988d6c8b3d0a0385164b372f202cdeb4a5849
- Cited by
- 2,227
- References
- 93
- Citations by year
- 2026: 219 · 2025: 353 · 2024: 306 · 2023: 321 · 2022: 310 · 2021: 337 · 2020: 203 · 2019: 99 · 2018: 50 · 2017: 18 · 2016: 9
- FWCI
- 48.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- New Journal of Physics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Army Research Office (W911NF-15-1-0256)
- Air Force Office of Scientific Research (FA9550-12-1-0046)
- Office of Naval Research Global (N00014-16-1-2008)
- Defense Security Science Engineering Fellowship (N00014-16-1-2008)
- Lawrence Berkeley National Laboratory (Luis W. Alvarez Fellowship in Compu)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Keywords
- hybrid quantum-classical algorithms, unitary coupled cluster, universal gate set, truncation, correlated sampling, derivative-free optimization
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
