VQE method: a short survey and recent developments
Open accessAbstract
The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-classical computational approach to find eigenvalues of a Hamiltonian. VQE has been proposed as an alternative to fully quantum algorithms such as quantum phase estimation (QPE) because fully quantum algorithms require quantum hardware that will not be accessible in the near future. VQE has been successfully applied to solve the electronic Schrödinger equation for a variety of small molecules. However, the scalability of this method is limited by two factors: the complexity of the quantum circuits and the complexity of the classical optimization problem. Both of these factors are affected by the choice of the variational ansatz used to represent the trial wave function. Hence, the construction of an efficient ansatz is an active area of research. Put another way, modern quantum computers are not capable of executing deep quantum circuits produced by using currently available ansatzes for problems that map onto more than several qubits. In this review, we present recent developments in the field of designing efficient ansatzes that fall into two categories—chemistry–inspired and hardware–efficient—that produce quantum circuits that are easier to run on modern hardware. We discuss the shortfalls of ansatzes originally formulated for VQE simulations, how they are addressed in more sophisticated methods, and the potential ways for further improvements.
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Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer.
“Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer.”
From On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification (2022) · cites this paper as [15]
Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for quantum chemistry applications in the NISQ era.
“Within the Noisy Intermediate-Scale Quantum (NISQ) era, Variational Quantum Eigensolvers (VQEs) [5] have emerged as one of the most promising algorithms for quantum chemistry applications. [6,7]”
From Limitations of Quantum Hardware for Molecular Energy Estimation Using VQE (2026) · cites this paper as [7]
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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 · see citing works
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 444 · see citing works
On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification
Sebastianelli, A., Zaidenberg, D. A., Spiller, D., Le Saux, B., & Ullo, S. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3134785 · Cited by 162 · see citing works
Quantum Machine Learning—An Overview
Tychola, K. A., Kalampokas, Τ., & Papakostas, G. A. (2023) · Electronics · doi:10.3390/electronics12112379 · Cited by 132 · see citing works
Recent Advances for Quantum Neural Networks in Generative Learning
Tian, J., Sun, X., Du, Y., Zhao, S., Liu, Q., Zhang, K., Yi, W., Huang, W., Wang, C., Wu, X., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3272029 · Cited by 116 · see citing works
Quantum computing for smart grid applications
Ullah, M. H., Eskandarpour, R., Zheng, H., & Khodaei, A. (2022) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.12602 · Cited by 107 · see citing works
Huang, H.-L., Xu, X., Guo, C., Tian, G., Wei, S.-J., Sun, X., Bao, W., & Long, G. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-2057-y · Cited by 101 · see citing works
Grimsley, H. R., Barron, G. S., Barnes, E., Economou, S. E., & Mayhall, N. J. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00681-0 · Cited by 93 · see citing works
Biology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87 · see citing works
Variational quantum eigensolver with reduced circuit complexity
Zhang, Y., Cincio, Ł., Negre, C. F. A., Czarnik, P., Coles, P. J., Anisimov, P. M., Mniszewski, S. M., Tretiak, S., & Dub, P. A. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00599-z · Cited by 75 · see citing works
Quantum computing of the Li6 nucleus via ordered unitary coupled clusters
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 72 · see citing works
Distributed Implementation of Full Configuration Interaction for One Trillion Determinants
Gao, H. M., Imamura, S., Kasagi, A., & Yoshida, E. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01190 · Cited by 62 · see citing works
Quantum computing with and for many-body physics
Ayral, T., Besserve, P., Denis Lacroix, A., & Guzman, E. A. R. (2023) · The European Physical Journal A · doi:10.1140/epja/s10050-023-01141-1 · Cited by 54 · see citing works
Quantum computing algorithms: getting closer to critical problems in computational biology
Marchetti, L., Nifosì, R., Martelli, P. L., Da Pozzo, E., Cappello, V., Banterle, F., Trincavelli, M. L., Martini, C., & D’Elia, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac437 · Cited by 47 · see citing works
Optimal control of quantum thermal machines using machine learning
Khait, I., Carrasquilla, J., & Segal, D. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.l012029 · Cited by 40 · see citing works
Quantum computing in pharma: A multilayer embedding approach for near future applications
Izsák, R., Riplinger, C., Blunt, N. S., de Souza, B., Holzmann, N., Crawford, O., Camps, J., Neese, F., & Schöpf, P. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.26958 · Cited by 40 · see citing works
Quantum utility – definition and assessment of a practical quantum advantage
Herrmann, N., Arya, D., Doherty, M. W., Mingare, A., Pillay, J. C., Preis, F., & Prestel, S. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00028 · Cited by 42 · see citing works
Quantum Computing for Climate Resilience and Sustainability Challenges
Ho, K. T. M., Chen, K.-C., Lee, L., Burt, F., Yu, S., & Lee, P.-H. H. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10289 · Cited by 42 · see citing works
Unitary Selective Coupled-Cluster Method
Fedorov, D. A., Alexeev, Y., Gray, S. K., & Otten, M. (2022) · Quantum · doi:10.22331/q-2022-05-02-703 · Cited by 34 · see citing works
Self-citationReal-Time Krylov Theory for Quantum Computing Algorithms
Shen, Y., Klymko, K., Sud, J., Williams‐Young, D. B., de Jong, W. A., & Tubman, N. M. (2023) · Quantum · doi:10.22331/q-2023-07-25-1066 · Cited by 32 · see citing works
Variational quantum eigensolver techniques for simulating carbon monoxide oxidation
Sapova, M. D., & Fedorov, A. K. (2022) · Communications Physics · doi:10.1038/s42005-022-00982-4 · Cited by 32 · see citing works
Pulse based Variational Quantum Optimal Control for hybrid quantum computing
de Keijzer, R., Tse, O. T. C., & Kokkelmans, S. J. J. M. F. (2023) · Quantum · doi:10.22331/q-2023-01-26-908 · Cited by 34 · see citing works
Sugisaki, K., Kato, T., Minato, Y., Okuwaki, K., & Mochizuki, Y. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp04318h · Cited by 29 · see citing works
CNOT-Efficient Circuits for Arbitrary Rank Many-Body Fermionic and Qubit Excitations
Magoulas, I., & Evangelista, F. A. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01016 · Cited by 28 · see citing works
Molecular Energy Landscapes of Hardware-Efficient Ansätze in Quantum Computing
Boy, C., & Wales, D. J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01057 · Cited by 27 · see citing works
Lim, H., Kang, D. H., Kim, J., Pellow-Jarman, A., McFarthing, S., Pellow-Jarman, R., Jeon, H.-N., Oh, B., Rhee, J. K., & No, K. T. (2024) · Scientific Reports · doi:10.1038/s41598-024-52926-3 · Cited by 31 · see citing works
Nearly optimal state preparation for quantum simulations of lattice gauge theories
Kane, C. F., Gomes, N., & Kreshchuk, M. (2024) · Physical Review A · doi:10.1103/physreva.110.012455 · Cited by 24 · see citing works
Crawford, O., Cruise, J. R., Mertig, N., & González-Zalba, M. F. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00679-8 · Cited by 24 · see citing works
Using Differential Evolution to avoid local minima in Variational Quantum Algorithms
Faílde, D., Viqueira, J. D., Juane, M. M., & Gómez, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-43404-3 · Cited by 22 · see citing works
Calibrating the role of entanglement in variational quantum circuits
Nakhl, A. C., Quella, T., & Usman, M. (2024) · Physical Review A · doi:10.1103/physreva.109.032413 · Cited by 22 · see citing works
Fitzpatrick, A., Nykänen, A., Talarico, N. W., Lunghi, A., Maniscalco, S., García-Pérez, G., & Knecht, S. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c05882 · Cited by 22 · see citing works
Quantum simulation of bosons with the contracted quantum eigensolver
Wang, Y., Sager-Smith, L. M., & Mazziotti, D. A. (2023) · New Journal of Physics · doi:10.1088/1367-2630/acf9c3 · Cited by 23 · see citing works
Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models
Mukherjee, A., Berthusen, N., Getelina, J. C., Orth, P. P., & Yao, Y. (2023) · Communications Physics · doi:10.1038/s42005-022-01089-6 · Cited by 21 · see citing works
Modeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective
Hariharan, S., Kinge, S., & Visscher, L. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01212 · Cited by 20 · see citing works
Sennane, W., Piquemal, J., & Rančić, M. J. (2023) · Physical Review A · doi:10.1103/physreva.107.012416 · Cited by 19 · see citing works
The Future of Drug Development with Quantum Computing
Bonde, B. K., Patil, P., & Choubey, B. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3449-3_7 · Cited by 19 · see citing works
Variational quantum eigensolvers in the era of distributed quantum computers
Khait, I., Tham, E., Segal, D., & Brodutch, A. (2023) · Physical Review A · doi:10.1103/physreva.108.l050401 · Cited by 18 · see citing works
Rahman, M. A., Akter, M. S., Miller, E., Timofti, B., Shahriar, H., Masum, M., & Wu, F. (2024) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac61105.2024.00144 · Cited by 18 · see citing works
Quasiparticle Cooling Algorithms for Quantum Many-Body State Preparation
Lloyd, J., Michailidis, A. A., Mi, X., Smelyanskiy, V., & Abanin, D. A. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.010361 · Cited by 21 · see citing works
Shell-model study of58Ni using quantum computing algorithm
Bhoy, B., & Stevenson, P. D. (2024) · New Journal of Physics · doi:10.1088/1367-2630/ad5756 · Cited by 17 · see citing works
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 17 · see citing works
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 16 · see citing works
Review of ansatz designing techniques for variational quantum algorithms
Qin, J. (2023) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2634/1/012043 · Cited by 20 · see citing works
Extending the variational quantum eigensolver to finite temperatures
Selisko, J., Amsler, M., Hammerschmidt, T., Drautz, R., & Eckl, T. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/ad1340 · Cited by 20 · see citing works
Efficient Quantum Circuit Cutting by Neglecting Basis Elements
Chen, D. T., Hansen, E. H., Li, X., Kulkarni, V. R., Chaudhary, V., Ren, B. W., Guan, Q., Kuppannagari, S. R., Liu, J., & Xu, S. (2023) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw59300.2023.00091 · Cited by 17 · see citing works
Backtesting Quantum Computing Algorithms for Portfolio Optimization
Carrascal, G., Hernamperez, P., Botella, G., & Barrio, A. A. D. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3337328 · Cited by 15 · see citing works
Variational quantum algorithms for scanning the complex spectrum of non-Hermitian systems
Xie, X.-D., Xue, Z., & Zhang, D.-B. (2024) · Frontiers of Physics · doi:10.1007/s11467-023-1382-3 · Cited by 15 · see citing works
Wang, S., Wang, P., Li, G., Zhao, S., Zhao, D., Wang, J., Yuan, F., Dou, M., Gu, Y., Wu, Y.-C., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4185-1 · Cited by 15 · see citing works
Blueprint for a digital-analog variational quantum eigensolver using Rydberg atom arrays
Michel, A., Grijalva, S., Henriet, L., Domain, C., & Browaeys, A. (2023) · Physical Review A · doi:10.1103/physreva.107.042602 · Cited by 14 · see citing works
Sawaya, N. P. D., Schmitz, A. T., & Hadfield, S. (2023) · Quantum · doi:10.22331/q-2023-09-14-1111 · Cited by 16 · see citing works
Accurate and efficient calculations of Hellmann–Feynman forces for quantum computation
Lai, J., Fan, Y., Fu, Q., Li, Z., & Yang, J. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0167444 · Cited by 14 · see citing works
Roh, J., Oh, S., Lee, D., Joo, C., Park, J., Moon, I., Ro, I., & Kim, J. (2024) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.3c07496 · Cited by 14 · see citing works
Kiser, M., Schroeder, A. K., Anselmetti, G.-L., Kumar, C., Moll, N., Streif, M., & Vodola, D. (2024) · New Journal of Physics · doi:10.1088/1367-2630/ad2f67 · Cited by 14 · see citing works
Mao, R., Tian, G., & Sun, X. (2024) · Communications Physics · doi:10.1038/s42005-024-01798-0 · Cited by 14 · see citing works
Mehendale, S. G., Peng, B., Govind, N., & Alexeev, Y. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c00550 · Cited by 14 · see citing works
Self-citationQ-BEEP: Quantum Bayesian Error Mitigation Employing Poisson Modeling over the Hamming Spectrum
Stein, S., Wiebe, N., Ding, Y., Ang, J., & Li, A. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589043 · Cited by 14 · see citing works
Leakage Reduces Device Coherence Demands for Pulse-Level Molecular Simulations
Asthana, A., Liu, C., Meitei, O. R., Economou, S. E., Barnes, E., & Mayhall, N. J. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.19.064071 · Cited by 20 · see citing works
Quantum Circuit Matrix Product State Ansatz for Large-Scale Simulations of Molecules
Fan, Y., Liu, J., Li, Z., & Yang, J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00068 · Cited by 15 · see citing works
Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers
Ghosh, K., Kumar, S., Nirmal, M. R., & Yamijala, S. S. R. K. C. (2023) · ACS Omega · doi:10.1021/acsomega.3c07364 · Cited by 12 · see citing works
Programmable adiabatic demagnetization for systems with trivial and topological excitations
Matthies, A., Rudner, M. S., Rosch, A., & Berg, E. (2024) · Quantum · doi:10.22331/q-2024-10-23-1505 · Cited by 16 · see citing works
Implicit Solvent Sample-Based Quantum Diagonalization
Kaliakin, D. S., Shajan, A., Liang, F., & Merz, K. M. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c01030 · Cited by 14 · see citing works
Graph optimization perspective for low-depth Trotter-Suzuki decomposition
Schmitz, A. T., Sawaya, N. P. D., Johri, S., & Matsuura, A. Y. (2024) · Physical Review A · doi:10.1103/physreva.109.042418 · Cited by 14 · see citing works
Quantum chemistry calculations using energy derivatives on quantum computers
Azad, U., & Singh, H. (2022) · Chemical Physics · doi:10.1016/j.chemphys.2022.111506 · Cited by 13 · see citing works
Boosting quantum amplitude exponentially in variational quantum algorithms
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Natural Orbitals and Sparsity of Quantum Mutual Information
Ratini, L., Capecci, C., & Guidoni, L. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01325 · Cited by 12 · see citing works
Algorithm-oriented qubit mapping for variational quantum algorithms
Ji, Y., Chen, X., Polian, I., & Ban, Y. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.034022 · Cited by 13 · see citing works
Excited-state molecular dynamics simulation based on variational quantum algorithms
Hirai, H. (2023) · Chemical Physics Letters · doi:10.1016/j.cplett.2023.140404 · Cited by 12 · see citing works
Robust measurement of wave function topology on NISQ quantum computers
Xiao, X., Freericks, J. K., & Kemper, A. F. (2023) · Quantum · doi:10.22331/q-2023-04-27-987 · Cited by 13 · see citing works
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Distributionally Robust Variational Quantum Algorithms With Shifted Noise
He, Z., Peng, B., Alexeev, Y., & Zhang, Z. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3409309 · Cited by 13 · see citing works
Self-citationSimulation of a Diels–Alder reaction on a quantum computer
Liepuoniute, I., Motta, M., Pellegrini, T., Rice, J. E., Gujarati, T. P., Gil, S., & Jones, G. O. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01314j · Cited by 12 · see citing works
Exploiting In-Constraint Energy in Constrained Variational Quantum Optimization
Hao, T., Shaydulin, R., Pistoia, M., & Larson, J. M. (2022) · IEEE/ACM International Workshop on Quantum Computing Software (QCS) · doi:10.1109/qcs56647.2022.00017 · Cited by 14 · see citing works
Linear-Scaling Quantum Circuits for Computational Chemistry
Magoulas, I., & Evangelista, F. A. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00376 · Cited by 11 · see citing works
Implementation of the Projective Quantum Eigensolver on a Quantum Computer
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Sun, J., Cheng, L., & Li, W. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00886 · Cited by 10 · see citing works
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Unitary Coupled Cluster: Seizing the Quantum Moment
Magoulas, I., & Evangelista, F. A. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c02781 · Cited by 10 · see citing works
Gustafson, E., Sherbert, K., Florio, A., Shirali, K., Chen, Y., Lamm, H., Valgushev, S. N., Weichselbaum, A., Economou, S. E., Pisarski, R. D., et al. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.064002 · Cited by 13 · see citing works
Clifford circuit initialization for variational quantum algorithms
Cheng, M. H., Khosla, K. E., Self, C. N., Lin, M., Li, B. X., Medina, A. C., & Kim, M. S. (2025) · Physical Review A · doi:10.1103/physreva.111.062413 · Cited by 13 · see citing works
Pruned-ADAPT-VQE: Compacting Molecular Ansätze by Removing Irrelevant Operators
Vaquero-Sabater, N., Carreras, A., & Casanova, D. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00535 · Cited by 9 · see citing works
Coupled cluster method tailored with quantum computing
Erhart, L., Yoshida, Y., Khinevich, V. E., & Mizukami, W. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023230 · Cited by 8 · see citing works
Circuit-Efficient Qubit Excitation-Based Variational Quantum Eigensolver
Sun, Z., Li, X., Liu, J., Li, Z., & Yang, J. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00119 · Cited by 8 · see citing works
BenchQC: A Benchmarking Toolkit for Quantum Computation
Pollard, N., & Choudhary, K. K. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70202 · Cited by 8 · see citing works
Perspective on Many-Body Methods for Molecular Polaritonic Systems
Bauman, N. P., Cunha, L. A., DePrince, A. E., Flick, J., Foley, J. J., Govind, N., Groenhof, G., Hoffmann, N. M., Kowalski, K., Li, X., et al. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00801 · Cited by 8 · see citing works
Self-citationMixed quantum-classical dynamics for near term quantum computers
Bultrini, D., & Vendrell, O. (2023) · Communications Physics · doi:10.1038/s42005-023-01451-2 · Cited by 8 · see citing works
Rapid quantum approaches for combinatorial optimisation inspired by optimal state-transfer
Banks, R. J., Browne, D. E., & Warburton, P. A. (2024) · Quantum · doi:10.22331/q-2024-02-13-1253 · Cited by 8 · see citing works
Variational Adiabatic Transport of Tensor Networks
Kim, H., Fishman, M., & Sels, D. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.020361 · Cited by 9 · see citing works
Simulator Demonstration of Large Scale Variational Quantum Algorithm on HPC Cluster
Morita, M., Tomita, Y., Koyama, J., & Kimura, K. (2024) · IEEE Access · doi:10.1109/access.2024.3415168 · Cited by 8 · see citing works
Noise-robust ground state energy estimates from deep quantum circuits
Vallury, H. J., Jones, M. A., White, G. A. L., Creevey, F. M., Hill, C. D., & Hollenberg, L. C. L. (2023) · Quantum · doi:10.22331/q-2023-09-11-1109 · Cited by 8 · see citing works
Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective
Carbonelli, C., Felderer, M., Jung, M., Lobe, E., Lochau, M., Luber, S. M., Mauerer, W., Ramler, R., Schaefer, I., & Schroth, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-64136-7_12 · Cited by 7 · see citing works
A quantum annealing protocol to solve the nuclear shell model
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Molecular Symmetry in VQE: A Dual Approach for Trapped-Ion Simulations of Benzene
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Max-cut Clustering Utilizing Warm-Start QAOA and IBM RuntimePreprint
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Parametrization and optimizability of pulse-level variational quantum eigensolvers
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Molecular electronic structure calculation via a quantum computer
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Optimization of the variational quantum eigensolver for quantum chemistry applications
de Keijzer, R., Colussi, V. E., Škorić, B., & Kokkelmans, S. J. J. M. F. (2022) · AVS Quantum Science · doi:10.1116/5.0076435 · Cited by 6 · see citing works
- Authors
- Fedorov, D. A., Peng, B., Govind, N., & Alexeev, Y.
- Affiliations
- Oak Ridge Associated Universities (US)
- Pacific Northwest National Laboratory (US)
- Argonne National Laboratory (US)
- Corresponding author
- Fedorov, Dmitry A.
- Peng, Bo
- Govind, Niranjan
- Alexeev, Yuri
- Venue
- Materials Theory
- Published
- 2022-01-06
- Volume
- 6
- Issue
- 1
- ISSN
- 2509-8012 (ISSN-L)
- OpenAlex
- W4206745400
- Cited by
- 224
- References
- 101
- Citations by year
- 2026: 28 · 2025: 64 · 2024: 52 · 2023: 53 · 2022: 24 · 2021: 3
- FWCI
- 15.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Materials Theory (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Science (DE-SC002024, DE-AC02-06CH11357)
- 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 › Advanced Chemical Physics Studies
- Keywords
- variational quantum eigensolver, variational ansatz, hybrid quantum-classical algorithms, hardware-efficient ansatz, electronic Schrödinger equation, molecular quantum chemistry, variational quantum algorithms, quantum chemistry on quantum computers
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 ›
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