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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.
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“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
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“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
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“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
“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]
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]
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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
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Chen, S., Cotler, J. S., Huang, H.-Y., & Li, J. Z. (2022) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs52979.2021.00063 · Cited by 79
Metcalf, M., Bauman, N. P., Kowalski, K., & de Jong, W. A. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00421 · Cited by 79
Nishi, H., Kosugi, T., & Matsushita, Y. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00409-y · Cited by 86
Motta, M., Gujarati, T. P., Rice, J. E., Kumar, A., Masteran, C., Latone, J. A., Lee, E., Valeev, E. F., & Takeshita, T. Y. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp04106h · Cited by 81
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Cai, Z. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.14.014059 · Cited by 78
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Zhang, S.-X., Hsieh, C.-Y., Zhang, S., & Yao, H. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ac28dd · Cited by 88
Izmaylov, A. F., Yen, T.-C., & Ryabinkin, I. G. (2019) · Chemical Science · doi:10.1039/c8sc05592k · Cited by 78
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Gomes, N., Zhang, F., Berthusen, N., Wang, C., Ho, K., Orth, P. P., & Yao, Y. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00666 · Cited by 72
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AbuGhanem, M., & Eleuch, H. (2024) · IEEE Access · doi:10.1109/access.2024.3432330 · Cited by 94
Gokhale, P., Angiuli, O., Ding, Y., Gui, K., Tomesh, T., Suchara, M., Martonosi, M., & Chong, F. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.13623 · Cited by 196
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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
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Matteo, O. D., McCoy, A. E., Gysbers, P., Miyagi, T., Woloshyn, R. M., & Navrátil, P. (2021) · Physical Review A · doi:10.1103/physreva.103.042405 · Cited by 77
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Wang, G., Koh, D. E., Johnson, P. D., & Cao, Y. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.010346 · Cited by 77
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Hamamura, I., & Imamichi, T. (2020) · npj Quantum Information · doi:10.1038/s41534-020-0284-2 · Cited by 80
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Bravyi, S., Gosset, D., & Movassagh, R. (2021) · Nature Physics · doi:10.1038/s41567-020-01109-8 · Cited by 89
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Endo, S., Zhao, Q., Li, Y., Benjamin, S., & Yuan, X. (2019) · Physical Review A · doi:10.1103/physreva.99.012334 · Cited by 72
Bharti, K., & Haug, T. (2021) · Physical Review A · doi:10.1103/physreva.104.042418 · Cited by 79
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Yu, N., & Palsberg, J. (2021) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3453483.3454061 · Cited by 89
Classically optimized Hamiltonian simulation
Keever, C. M., & Lubasch, M. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023146 · Cited by 92
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Sawaya, N. P. D., & Huh, J. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b01117 · Cited by 79
Accuracy and Resource Estimations for Quantum Chemistry on a Near-Term Quantum Computer
Kühn, M., Zanker, S., Deglmann, P., Marthaler, M., & Weiß, H. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00236 · Cited by 71
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Xia, R., & Kais, S. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abbc74 · Cited by 76
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Ciavarella, A. N., & Chernyshev, I. A. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.105.074504 · Cited by 103
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Kiss, O., Grossi, M., Lougovski, P., Sánchez, F. A., Vallecorsa, S., & Papenbrock, T. (2022) · Physical Review C · doi:10.1103/physrevc.106.034325 · Cited by 82
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Glasser, I., Pancotti, N., & Cirac, J. I. (2020) · IEEE Access · doi:10.1109/access.2020.2986279 · Cited by 129
Bukov, M. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.98.224305 · Cited by 72
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Self-citationA 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
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Uvarov, A. V., Kardashin, A. S., & Biamonte, J. D. (2020) · Physical Review A · doi:10.1103/physreva.102.012415 · Cited by 69
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Das, P., Tannu, S., Dangwal, S., & Qureshi, M. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480059 · Cited by 78
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Shaydulin, R., Ushijima‐Mwesigwa, H., Negre, C. F. A., Safro, I., Mniszewski, S. M., & Alexeev, Y. (2019) · Computer · doi:10.1109/mc.2019.2908942 · Cited by 73
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Fösel, T., Niu, M. Y., Marquardt, F., & Li, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07585 · Cited by 218
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Abohashima, Z., Elhosen, M., Houssein, E. H., & Mohamed, W. M. (2020) · arXiv · doi:10.48550/arxiv.2006.12270 · Cited by 68
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Weder, B., Barzen, J., Leymann, F., & Vietz, D. (2022) · Springer eBooks · doi:10.1007/978-3-031-05324-5_4 · Cited by 78
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Arrasmith, A., Cincio, L., Sornborger, A. T., Zurek, W. H., & Coles, P. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11417-0 · Cited by 66
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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
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