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
“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
“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]
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
Items 801–900 of 1,009
Implementation of measurement reduction for the variational quantum eigensolver
Ralli, A., Love, P. J., Tranter, A., & Coveney, P. V. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033195 · Cited by 22
Robust Control Optimization for Quantum Approximate Optimization Algorithms
Dong, Y., Meng, X., Lin, L., Kosut, R. L., & Whaley, K. B. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.130 · Cited by 19
Automated Quantum Hardware Selection for Quantum Workflows
Weder, B., Barzen, J., Leymann, F., & Salm, M. (2021) · Electronics · doi:10.3390/electronics10080984 · Cited by 19
Choi, S., & Izmaylov, A. F. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00218 · Cited by 21
“Design and Implementation of Efficient Quantum Support Vector Machine,”
Mafu, M., & Senekane, M. (2021) · 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698509 · Cited by 25
On-premises superconducting quantum computer for education and research
Rönkkö, J., Ahonen, O., Bergholm, V., Calzona, A., Geresdi, A., Heimonen, H., Heinsoo, J., Milchakov, V., Pogorzalek, S., Sarsby, M., et al. (2024) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-024-00243-z · Cited by 23
Smart, S. E., & Mazziotti, D. A. (2022) · Physical Review A · doi:10.1103/physreva.105.062424 · Cited by 17
AGP-based unitary coupled cluster theory for quantum computers
Khamoshi, A., Chen, G. P., Evangelista, F. A., & Scuseria, G. E. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac93ae · Cited by 21
Classical simulation of variational quantum classifiers using tensor rings
Peddireddy, D., Bansal, V., & Aggarwal, V. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110308 · Cited by 20
TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
Li, W., Allcock, J., Cheng, L., Zhang, S., Chen, Y., Mailoa, J. P., Shuai, Z., & Zhang, S. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00319 · Cited by 21
Kashif, M., & Al-Kuwari, S. M. (2023) · International Journal of Parallel Emergent and Distributed Systems · doi:10.1080/17445760.2023.2231163 · Cited by 22
Quantum simulations of SO(5) many-fermion systems using qudits
Illa, M., Robin, C., & A, M. J. S. (2023) · Physical Review C · doi:10.1103/physrevc.108.064306 · Cited by 23
Variational quantum algorithm for ergotropy estimation in quantum many-body batteries
Hoang, D. T., Metz, F., Thomasen, A. M. D., Anh-Tai, T. D., Busch, T., & Fogarty, T. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013038 · Cited by 18
Extending echo state property for quantum reservoir computing
Kobayashi, S., Tran, Q. H., & Nakajima, K. (2024) · Physical review. E · doi:10.1103/physreve.110.024207 · Cited by 18
Optimizing Shot Assignment in Variational Quantum Eigensolver Measurement
Zhu, L., Liang, S., Yang, C., & Li, X. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01113 · Cited by 28
Simulating $Z_{2}$ lattice gauge theory with the variational quantum thermalizer
Fromm, M., Philipsen, O., Spannowsky, M., et al. (2024) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-024-00232-2 · Cited by 18
Jensen, P. W. K., Kjellgren, E. R., Reinholdt, P., Ziems, K. M., Coriani, S., Kongsted, J., & Sauer, S. P. A. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00069 · Cited by 19
“In 52–54 UCC exhibits important features such as unitarity and a variational character, and it is believed that UCC results in a more multi-configurational wave function compared to coupled-cluster.”
BackgroundFeedback-based quantum algorithms for ground state preparation
Larsen, J. B., Grace, M., Baczewski, A., & Magann, A. B. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033336 · Cited by 24
Gyöngyösi, L., & Imre, S. (2025) · Quantum Information and Computation · doi:10.2478/qic-2025-0006 · Cited by 20
One-dimensional quantum computing with a ‘segmented chain’ is feasible with today’s gate fidelities
Li, Y., & Benjamin, S. C. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0074-2 · Cited by 19
Short-depth circuits for efficient expectation-value estimation
Roggero, A., & Baroni, A. (2020) · Physical Review A · doi:10.1103/physreva.101.022328 · Cited by 18
Implementation of a quantum transfer learning approach to image splicing detection
Gokhale, A., Pande, M. B., & Pramod, D. (2020) · International Journal of Quantum Information · doi:10.1142/s0219749920500240 · Cited by 18
Variational quantum solver employing the PDS energy functional
Peng, B., & Kowalski, K. (2021) · Quantum · doi:10.22331/q-2021-06-10-473 · Cited by 18
Many-body calculations for periodic materials via restricted Boltzmann machine-based VQE
Kanno, S., & Tada, T. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abe139 · Cited by 19
What the foundations of quantum computer science teach us about chemistry
McClean, J. R., Rubin, N. C., Lee, J., Harrigan, M. P., O’Brien, T. E., Babbush, R., Huggins, W. J., & Huang, H.-Y. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0060367 · Cited by 20
Self-citationA Quantum Interpretation of Bunched Logic & Quantum Separation Logic
Zhou, L., Barthe, G., Hsu, J., Ying, M., & Yu, N. (2021) · ACM/IEEE Symposium on Logic in Computer Science (LICS) · doi:10.1109/lics52264.2021.9470673 · Cited by 32
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Chang, S. Y., Herbert, S., Vallecorsa, S., Combarro, E. F., & Duncan, R. (2021) · Springer Link (Chiba Institute of Technology) · doi:10.1051/epjconf/202125103050/pdf · Cited by 18
How Parallel Circuit Execution Can Be Useful for NISQ Computing?
Niu, S., & Todri‐Sanial, A. (2022) · Design, Automation, and Test in Europe · doi:10.23919/date54114.2022.9774512 · Cited by 18
Quantum Circuit Evolution on NISQ Devices
Franken, L., Georgiev, B., Mücke, S., Wolter, M., Heese, R., Bauckhage, C., & Piatkowski, N. (2022) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec55065.2022.9870269 · Cited by 21
Quantum simulations of molecular systems with intrinsic atomic orbitals
Barison, S., Galli, D. E., & Motta, M. (2022) · Physical Review A · doi:10.1103/physreva.106.022404 · Cited by 23
Lowering qubit requirements for quantum simulations of fermionic systemsPreprint
Steudtner, M., & Wehner, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.07067 · Cited by 29
Optimizing Ansatz Design in QAOA for Max-cutPreprint
Majumdar, R., Madan, D., Bhoumik, D., Vinayagamurthy, D., Raghunathan, S., & Sur‐Kolay, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.02812 · Cited by 18
Mineh, L., & Montanaro, A. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.125117 · Cited by 22
Error mitigation on a near-term quantum photonic device
Su, D., Israel, R., Sharma, K., Qi, H., Dhand, I., & Brádler, K. (2021) · Quantum · doi:10.22331/q-2021-05-04-452 · Cited by 19
Beyond unital noise in variational quantum algorithms: noise-induced barren plateaus and limit sets
Singkanipa, P., & Lidar, D. A. (2025) · Quantum · doi:10.22331/q-2025-01-30-1617 · Cited by 31
“These algorithms leverage a customizable quantum circuit de-sign, integrating both quantum and classical computation capabilities.”
Matarèse, B. F. E., & Purushotham, A. D. (2025) · Quantum Reports · doi:10.3390/quantum7010009 · Cited by 21
“Hybrid Quantum–Classical Systems A promising pathway to overcoming these challenges lies in hybrid quantum–classical systems [485,486], which combine the strengths of classical machine learning with quantum simulations.”
Hassan, S. S., Khan, L. U., Park, Y. M., Guizani, M., Han, Z., Ratnarajah, T., & Hong, C. S. (2025) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2025.3610357 · Cited by 22
Quantum Circuits for the Preparation of Spin Eigenfunctions on Quantum Computers
Carbone, A., Galli, D. E., Motta, M., & Jones, B. (2022) · Symmetry · doi:10.3390/sym14030624 · Cited by 18
Fermionic approach to variational quantum simulation of Kitaev spin models
Jahin, A., Li, A. C. Y., Iadecola, T., Orth, P. P., Perdue, G., Macridin, A., Alam, M. S., & Tubman, N. M. (2022) · Physical Review A · doi:10.1103/physreva.106.022434 · Cited by 16
Tao, Y., Zeng, X., Fan, Y., Liu, J., Li, Z., & Yang, J. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c01738 · Cited by 19
Smart, S. E., & Mazziotti, D. A. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00446 · Cited by 19
Training Variational Quantum Circuits through Genetic Algorithms
Acampora, G., Chiatto, A., & Vitiello, A. (2022) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec55065.2022.9870242 · Cited by 18
Yordanov, Y. S., Barnes, C. H. W., & Arvidsson-Shukur, D. R. M. (2022) · Physical Review A · doi:10.1103/physreva.106.032434 · Cited by 24
Estimating Quantum Hamiltonians via Joint Measurements of Noisy Noncommuting Observables
McNulty, D., Maciejewski, F. B., & Oszmaniec, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.100801 · Cited by 18
Solution of SAT problems with the adaptive-bias quantum approximate optimization algorithm
Yu, Y., Cao, C., Wang, X., Shannon, N., & Joynt, R. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023147 · Cited by 21
Bayesian Optimization for QAOA
Tibaldi, S., Vodola, D., Tignone, E., & Ercolessi, E. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3325167 · Cited by 30
Iterative quantum algorithm for combinatorial optimization based on quantum gradient descent
Yi, X., Huo, J., Gao, Y., Fan, L., Zhang, R., & Cao, C. (2023) · Results in Physics · doi:10.1016/j.rinp.2023.107204 · Cited by 17
Benefits of Open Quantum Systems for Quantum Machine Learning
Olivera‐Atencio, M. L., Lamata, L., & Casado‐Pascual, J. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300247 · Cited by 18
Optimal Flight-Gate Assignment on a Digital Quantum Computer
Chai, Y., Funcke, L., Hartung, T., Jansen, K., Kühn, S., Stornati, P., & Stollenwerk, T. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.064025 · Cited by 17
Accelerated variational quantum eigensolver with joint Bell measurement
Cao, C., Yano, H., & Nakagawa, Y. O. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013205 · Cited by 17
Which Options Exist for NISQ-Friendly Linear Response Formulations?
Ziems, K. M., Kjellgren, E. R., Reinholdt, P., Jensen, P. W. K., Sauer, S. P. A., Kongsted, J., & Coriani, S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01402 · Cited by 17
“19 The NISQ device is used to perform measurements of specific expectation values or density matrix elements while the rest of the calculation is offloaded to the classical processors.”
MethodA probabilistic quantum algorithm for imaginary-time evolution based on Taylor expansion
Yi, X., Huo, J., Liu, G., Fan, L., Zhang, R., & Cao, C. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00347-0 · Cited by 17
“Initially, it addresses the optimization parameters of a unitary ansatz using McLachlan’s variational principle on a classical computer [35].”
Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations
Hsieh, C., Sun, Q., Zhang, S., & Lee, C. (2021) · npj Quantum Information · doi:10.1038/s41534-020-00347-1 · Cited by 21
Efficient two-electron ansatz for benchmarking quantum chemistry on a quantum computer
Smart, S. E., & Mazziotti, D. A. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023048 · Cited by 17
A variational quantum algorithm for Hamiltonian diagonalization
Zeng, J., Cao, C., Zhang, C., Xu, P., & Zeng, B. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac11a7 · Cited by 19
How to enhance quantum generative adversarial learning of noisy information
Braccia, P., Caruso, F., & Banchi, L. (2021) · New Journal of Physics · doi:10.1088/1367-2630/abf798 · Cited by 18
Complexity of Supersymmetric Systems and the Cohomology Problem
Cade, C., & Crichigno, P. M. (2024) · Quantum · doi:10.22331/q-2024-04-30-1325 · Cited by 39
QProv: A provenance system for quantum computing
Weder, B., Barzen, J., Leymann, F., Salm, M., & Wild, K. (2021) · IET Quantum Communication · doi:10.1049/qtc2.12012 · Cited by 22
Exploiting fermion number in factorized decompositions of the electronic structure Hamiltonian
McArdle, S., Campbell, E. T., & Su, Y. (2022) · Physical Review A · doi:10.1103/physreva.105.012403 · Cited by 23
Simulation of Condensed-Phase Spectroscopy with Near-Term Digital Quantum Computers
Lee, C., Hsieh, C., Zhang, S., & Shi, L. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00849 · Cited by 17
Q2Chemistry: A quantum computation platform for quantum chemistry
Fan, Y., Liu, J., Zeng, X., Xu, Z., Shang, H., Li, Z., & Yang, J. (2022) · JUSTC · doi:10.52396/justc-2022-0118 · Cited by 25
Quantum Computation of Hydrogen Bond Dynamics and Vibrational Spectra
Richerme, P., Revelle, M., Yale, C. G., Lobser, D., Burch, A. D., Clark, S., Saha, D., Lopez-Ruiz, M. A., Dwivedi, A., Smith, J. M., et al. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c01601 · Cited by 23
Laziness, barren plateau, and noises in machine learning
Liu, J., Lin, Z., & Jiang, L. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad35a3 · Cited by 26
Relation between fermionic and qubit mean fields in the electronic structure problem
Ryabinkin, I. G., Genin, S. N., & Izmaylov, A. F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5055357 · Cited by 17
Semantics for variational Quantum programming
Jia, X., Kornell, A., Lindenhovius, B., et al. (2022) · Proceedings of the ACM on Programming Languages · doi:10.1145/3498687 · Cited by 16
Discovering optimal fermion-qubit mappings through algorithmic enumeration
Chiew, M., & Strelchuk, S. (2023) · Quantum · doi:10.22331/q-2023-10-18-1145 · Cited by 22
Variational Entanglement-Assisted Quantum Process Tomography with Arbitrary Ancillary Qubits
Xue, S., Wang, Y., Zhan, J., Wang, Y., Zeng, R., Ding, J., Shi, W., Liu, Y., Liu, Y., Huang, A., et al. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.133601 · Cited by 20
Quantum Computing: Progress and Innovation
Byrd, G. T., & Ding, Y. (2023) · Computer · doi:10.1109/mc.2022.3217021 · Cited by 16
Improved Variational Quantum Eigensolver Via Quasidynamical Evolution
Jattana, M. S., Jin, F., Raedt, H. A. D., & Michielsen, K. F. L. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.19.024047 · Cited by 26
Efficient quantum analytic nuclear gradients with double factorization
Hohenstein, E. G., Oumarou, O., Al-Saadon, R., Anselmetti, G.-L., Scheurer, M., Gogolin, C., & Parrish, R. M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0137167 · Cited by 19
Variational quantum solutions to the Shortest Vector Problem
Albrecht, M. R., Prokop, M., Shen, Y., & Wallden, P. (2023) · Quantum · doi:10.22331/q-2023-03-02-933 · Cited by 16
Optimizing the information extracted by a single qubit measurement
Polla, S., Anselmetti, G.-L., & O’Brien, T. E. (2023) · Physical Review A · doi:10.1103/physreva.108.012403 · Cited by 17
Amsler, M., Deglmann, P., Degroote, M., Kaicher, M., Kiser, M., Kühn, M., Kumar, C., Maier, A., Samsonidze, G. G., Schroeder, A. K., et al. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0146934 · Cited by 27
Quantum-classical eigensolver using multiscale entanglement renormalization
Miao, Q., & Barthel, T. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033141 · Cited by 19
Solving Nonnative Combinatorial Optimization Problems Using Hybrid Quantum–Classical Algorithms
Wurtz, J., Sack, S. H., & Wang, S.-T. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3443660 · Cited by 21
Wulff, A., Chen, B., Steinberg, M., Tang, Y., Möller, M., & Feld, S. (2024) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2024.117380 · Cited by 19
Beyond MP2 initialization for unitary coupled cluster quantum circuits
Hirsbrunner, M. R., Chamaki, D., Mullinax, J. W., & Tubman, N. M. (2024) · Quantum · doi:10.22331/q-2024-11-26-1538 · Cited by 18
Optimal Tradeoffs for Estimating Pauli Observables
Chen, S., Gong, W., & Ye, Q. (2024) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs61266.2024.00072 · Cited by 48
“We have already discussed prior theoretical work on Pauli shadow tomography and mention here the plethora of works on applications of this to finding ground states, e.g. in quantum chemistry [30], [31], [35], [50], [52], [53], [55], [60], [65]. b) Quantum memory trade-offs for learning.”
Li, S., Cui, J., & Ren, J. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129111 · Cited by 16
Grimsley, H. R., & Evangelista, F. A. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad9fa2 · Cited by 20
Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping
Gresch, A., & Kliesch, M. (2025) · Nature Communications · doi:10.1038/s41467-024-54859-x · Cited by 20
Majid, B., Sofi, S. A., & Jabeen, Z. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00266-4 · Cited by 17
Quantum computing for energy correlators
Lee, K., Turro, F., & Yao, X. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.054514 · Cited by 38
Efficient quantum simulation of QCD jets on the light front
Qian, W., Li, M., Salgado, C. A., & Kreshchuk, M. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.096001 · Cited by 16
Efficient qubit routing for a globally connected trapped ion quantum computer
Herbert, S. (2020) · Sussex Research Online (University of Sussex) · Cited by 16
Variational wave functions for Sachdev-Ye-Kitaev models
Haldar, A., Tavakol, O., & Scaffidi, T. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.023020 · Cited by 16
Quantum simulation of molecules without fermionic encoding of the wave function
Mazziotti, D. A., Smart, S. E., & Mazziotti, A. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac3573 · Cited by 14
Quantum Error Mitigation Relying on Permutation Filtering
Xiong, Y., Ng, S. X., & Hanzo, L. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3132914 · Cited by 17
Miki, T., Okita, R., Shimada, M., Tsukayama, D., & Shirakashi, J. (2022) · AIP Advances · doi:10.1063/5.0077706 · Cited by 16
Markov chain Monte Carlo enhanced variational quantum algorithms
Patti, T. L., Shehab, O., Najafi, K., & Yelin, S. F. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/aca821 · Cited by 20
Hybrid Quantum Variational Autoencoders for Representation Learning
Rivas, P., Liang, Z., & Orduz, J. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00085 · Cited by 18
Conditional Born machine for Monte Carlo event generation
Kiss, O., Grossi, M., Kajomovitz, E., & Vallecorsa, S. (2022) · Physical Review A · doi:10.1103/physreva.106.022612 · Cited by 25
ScQ cloud quantum computation for generating Greenberger-Horne-Zeilinger states of up to 10 qubits
Chen, C.-T., Shi, Y., Xiang, Z., Wang, Z., Li, T., Sun, H.-Y., He, T.-S., Song, X., Zhao, S., Zheng, D., et al. (2022) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-1972-1 · Cited by 16
Optimizing counterdiabaticity by variational quantum circuits
Sun, D., Chandarana, P., Xin, Z., & Chen, X. (2022) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2021.0282 · Cited by 20
Quantum approximate optimization algorithm for Bayesian network structure learning
Soloviev, V. P., Bielza, C., & Larrañaga, P. (2022) · Quantum Information Processing · doi:10.1007/s11128-022-03769-2 · Cited by 18
Zhang, J., Ferguson, R., Kühn, S., Haase, J. F., Wilson, C. M., Jansen, K., & Muschik, C. A. (2023) · Quantum · doi:10.22331/q-2023-10-23-1148 · Cited by 23
Classically optimized variational quantum eigensolver with applications to topological phases
Okada, K. N., Osaki, K., Mitarai, K., & Fujii, K. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043217 · Cited by 23
Mullinax, J. W., & Tubman, N. M. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0251601 · Cited by 23
Quantum humanities: a vision for quantum computing in digital humanities
Barzen, J., & Leymann, F. (2019) · SICS Software-Intensive Cyber-Physical Systems · doi:10.1007/s00450-019-00419-4 · Cited by 16
Certified variational quantum algorithms for eigenstate preparation
Kardashin, A., Uvarov, A., Yudin, D. I., & Biamonte, J. (2020) · Physical Review A · doi:10.1103/physreva.102.052610 · Cited by 16
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