The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Variational Quantum Computation of Excited States
Open accessAbstract
The calculation of excited state energies of electronic structure Hamiltonians has many important applications, such as the calculation of optical spectra and reaction rates. While low-depth quantum algorithms, such as the variational quantum eigenvalue solver (VQE), have been used to determine ground state energies, methods for calculating excited states currently involve the implementation of high-depth controlled-unitaries or a large number of additional samples. Here we show how overlap estimation can be used to deflate eigenstates once they are found, enabling the calculation of excited state energies and their degeneracies. We propose an implementation that requires the same number of qubits as VQE and at most twice the circuit depth. Our method is robust to control errors, is compatible with error-mitigation strategies and can be implemented on near-term quantum computers.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In fact, amplifying the noise strength (that is increasing the quantity s ), deteriorates the solution quality.”
From Variational quantum algorithms (2021) · cites this paper as [106]
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
“Due to the presence of hardware noise the estimated energy ˜ E G has a gap with the true energy.”
From Variational quantum algorithms (2021) · cites this paper as [106]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“The VQE algorithm can be used to estimate the ground state energy E G of a molecule.”
From Variational quantum algorithms (2021) · cites this paper as [106]
The cited source says what the claim attributes to it.
In the citing paper
“Combined with the unitary coupled-cluster ansatz, the orthogonally constrained VQE can find excited states of small molecules (Higgott et al. , 2019; Lee et al. , 2018).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Higgott et al. , 2019
In the citing paper
“For example, a recent paper 381 initiated the use of low-rank tensor decompositions for improving the performance of certain quantum algorithms for chemistry simulation.”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [381]
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
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
(1999) · Cited by 12,854
Analysis of a complex of statistical variables into principal components.
Hotelling, H. (1933) · Journal of Educational Psychology · doi:10.1037/h0071325 · Cited by 9,691
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Szabó, A., & Ostlund, N. S. (1982) · Cited by 6,239
The Activated Complex in Chemical Reactions
Eyring, H. (1935) · The Journal of Chemical Physics · doi:10.1063/1.1749604 · Cited by 5,236
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 · cited 4×
“a folded spectrum method [30] which requires finding the expectation of the squared Hamiltonian with quadratically more terms, or symmetrybased methods which are non-systematic [23].”
BackgroundMethodInfluential
Cited by
Sorted by citations
Items 101–200 of 463
Simulating the Electronic Structure of Spin Defects on Quantum Computers
Huang, B., Govoni, M., & Galli, G. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.010339 · Cited by 42
Variational quantum algorithms for dimensionality reduction and classification
Liang, J., Shen, S., Li, M., & Li, L. (2020) · Physical Review A · doi:10.1103/physreva.101.032323 · Cited by 42
Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials
Sajjan, M., Sureshbabu, S. H., & Kais, S. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c06246 · Cited by 43
Construction of Green's functions on a quantum computer: Quasiparticle spectra of molecules
Kosugi, T., & Matsushita, Y. (2020) · Physical Review A · doi:10.1103/physreva.101.012330 · Cited by 45
Variational quantum algorithm for molecular geometry optimization
Delgado, A., Arrazola, J. M., Jahangiri, S., Niu, Z., Izaac, J., Roberts, C., & Killoran, N. (2021) · Physical Review A · doi:10.1103/physreva.104.052402 · Cited by 44
Symmetry enhanced variational quantum spin eigensolver
Lyu, C., Xu, X., Yung, M., & Bayat, A. (2023) · Quantum · doi:10.22331/q-2023-01-19-899 · Cited by 43
The Useful Quantum Computing Techniques for Artificial Intelligence Engineers
Choi, J., Oh, S., & Kim, J. (2020) · 2020 International Conference on Information Networking (ICOIN) · doi:10.1109/icoin48656.2020.9016555 · Cited by 39
Variational quantum eigensolver for dynamic correlation functions
Chen, H., Nusspickel, M., Tilly, J., & Booth, G. H. (2021) · Physical Review A · doi:10.1103/physreva.104.032405 · Cited by 42
Solving hadron structures using the basis light-front quantization approach on quantum computers
Qian, W., Basili, R., Pal, S., Luecke, G. R., & Vary, J. P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043193 · Cited by 40
Quantum Architecture Search with Meta‐Learning
He, Z., Chen, C., Li, L., Zheng, S., & Situ, H. (2022) · Advanced Quantum Technologies · doi:10.1002/qute.202100134 · Cited by 44
Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
Wines, D., Gurunathan, R. L., Garrity, K. F., DeCost, B., Biacchi, A. J., Tavazza, F., & Choudhary, K. K. (2023) · Applied Physics Reviews · doi:10.1063/5.0159299 · Cited by 42
Koridon, E., Yalouz, S., Senjean, B., Buda, F., O’Brien, T. E., & Visscher, L. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033127 · Cited by 44
Variational quantum algorithm with information sharing
Self, C. N., Khosla, K. E., Smith, A. W. R., Sauvage, F., Haynes, P. D., Knolle, J., Mintert, F., & Kim, M. S. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00452-9 · Cited by 40
Selected topics of quantum computing for nuclear physics*
Zhang, D.-B., Xing, H., Yan, H., Wang, E., & Zhu, S.-L. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abd761 · Cited by 38
Bauman, N. P., Chládek, J., Veis, L., Pittner, J., & Kowalski, K. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abf602 · Cited by 38
Gocho, S., Nakamura, H., Kanno, S., Gao, Q., Kobayashi, T., Inagaki, T., & Hatanaka, M. (2023) · npj Computational Materials · doi:10.1038/s41524-023-00965-1 · Cited by 41
Variational quantum classifiers through the lens of the Hessian
Sen, P., Bhatia, A. S., Bhangu, K. S., & Elbeltagi, A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0262346 · Cited by 38
Hamiltonian operator approximation for energy measurement and ground state preparation
(21925472), T. B., & Kyriienko, O. (2025) · Figshare · Cited by 39
Tilly, J., Jones, G. A., Chen, H., Wossnig, L., & Grant, E. (2020) · Physical Review A · doi:10.1103/physreva.102.062425 · Cited by 43
Clemente, G., Crippa, A., & Jansen, K. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.114511 · Cited by 33
A Variational Quantum Linear Solver Application to Discrete Finite-Element Methods
Trahan, C. J., Loveland, M., Davis, N. A., & Ellison, E. (2023) · Entropy · doi:10.3390/e25040580 · Cited by 40
Preserving Symmetries for Variational Quantum Eigensolvers in the Presence of Noise
Barron, G. S., Gard, B., Altman, O. J., Mayhall, N. J., Barnes, E., & Economou, S. E. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.034003 · Cited by 38
Quantum computation of silicon electronic band structure
Cerasoli, F. T., Sherbert, K., Sławińska, J., & Nardelli, M. B. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp04008h · Cited by 38
Zhang, F., Gomes, N., Berthusen, N., Orth, P. P., Wang, C.-Z., Ho, K.-M., & Yao, Y.-X. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.013039 · Cited by 34
Aydeniz, K. Y., Gard, B., Jakowski, J., Majumder, S., Barron, G. S., Siopsis, G., Humble, T. S., & Pooser, R. C. (2021) · Advanced Quantum Technologies · doi:10.1002/qute.202100012 · Cited by 40
Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks
Smith, A. W. R., Gray, J., & Kim, M. S. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.020348 · Cited by 32
Bosse, J. L., & Montanaro, A. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.094409 · Cited by 33
Quantum Simulation of Molecular Response Properties in the NISQ Era
Kumar, A., Asthana, A., Abraham, V., Crawford, T. D., Mayhall, N. J., Zhang, Y., Cincio, Ł., Tretiak, S., & Dub, P. A. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00731 · Cited by 35
Quantum natural gradient generalised to non-unitary circuitsPreprint
Koczor, B., & Benjamin, S. C. (2019) · arXiv (Cornell University) · Cited by 35
Noisy intermediate-scale quantum algorithm for semidefinite programming
Bharti, K., Haug, T., Vedral, V., & Kwek, L. C. (2022) · Physical Review A · doi:10.1103/physreva.105.052445 · Cited by 39
Quantum computation for periodic solids in second quantization
Ivanov, A. V., Sünderhauf, C., Holzmann, N., Ellaby, T., Kerber, R. N., Jones, G. A., & Camps, J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013200 · Cited by 39
Quantum technologies for climate change: Preliminary assessmentPreprint
Berger, C., Paolo, A. D., Forrest, T., Hadfield, S., Sawaya, N. P. D., Stęchły, M., & Thibault, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.05362 · Cited by 58
Variational quantum simulation of U(1) lattice gauge theories with qudit systems
Popov, P. P., Meth, M., Lewenstein, M., Hauke, P., Ringbauer, M., Zohar, E., & Kasper, V. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013202 · Cited by 34
A GNN-based predictor for quantum architecture search
He, Z., Zhang, X., Chen, C., Huang, Z., Zhou, Y., & Situ, H. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-03881-x · Cited by 31
Folded Spectrum VQE: A Quantum Computing Method for the Calculation of Molecular Excited States
Tazi, L. C., & Thom, A. J. W. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01378 · Cited by 41
Sugisaki, K., Sakai, C., Toyota, K., Sato, K., Shiomi, D., & Takui, T. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp03156b · Cited by 31
Patterns for Hybrid Quantum Algorithms
Weigold, M., Barzen, J., Leymann, F., & Vietz, D. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-87568-8_2 · Cited by 30
Sugisaki, K., Kato, T., Minato, Y., Okuwaki, K., & Mochizuki, Y. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp04318h · Cited by 32
Probing many-body localization by excited-state variational quantum eigensolver
Liu, S., Zhang, S., Hsieh, C., Zhang, S., & Yao, H. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.107.024204 · Cited by 35
Hamiltonian Operator Approximation for Energy Measurement and Ground-State Preparation
Bespalova, T. A., & Kyriienko, O. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.030318 · Cited by 44
Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model
Li, A. C. Y., Alam, M. S., Iadecola, T., Jahin, A., Job, J., Kürkçüog̃lu, D. M., Li, R., Orth, P. P., Özgüler, A. B., Perdue, G., et al. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033071 · Cited by 34
Zhang, F., Gomes, N., Berthusen, N., Orth, P. P., Wang, C., Ho, K., & Yao, Y. (2021) · Iowa State University Digital Repository (Iowa State University) · Cited by 27
State preparation and evolution in quantum computing: A perspective from Hamiltonian moments
Aulicino, J. C., Keen, T., & Peng, B. (2021) · International Journal of Quantum Chemistry · doi:10.1002/qua.26853 · Cited by 30
NISQ Quantum Computing: A Security-Centric Tutorial and Survey [Feature]
Chen, F., Jiang, L., Müller, H. A., Richerme, P., Chu, C., Fu, Z., & Min, Y. (2024) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2024.3349665 · Cited by 32
Ramôa, M., Anastasiou, P. G., Santos, L. P., Mayhall, N. J., Barnes, E., & Economou, S. E. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01039-4 · Cited by 48
HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware
Sawaya, N. P. D., Marti-Dafcik, D., Ho, Y., Tabor, D. P., Bernal, D. E., Magann, A. B., Premaratne, S., Dubey, P. K., Matsuura, A. Y., Bishop, N. C., et al. (2024) · Quantum · doi:10.22331/q-2024-12-11-1559 · Cited by 39
Predicting excited states from ground state wavefunction by supervised quantum machine learning
Kawai, H., & Nakagawa, Y. O. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aba183 · Cited by 27
Omiya, K., Nakagawa, Y. O., Koh, S., Mizukami, W., Gao, Q., & Kobayashi, T. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00877 · Cited by 36
Electronic excited states from a variance-based contracted quantum eigensolver
Wang, Y., & Mazziotti, D. A. (2023) · Physical Review A · doi:10.1103/physreva.108.022814 · Cited by 30
Kanno, K., Kohda, M., Imai, R., Koh, S., Mitarai, K., Mizukami, W., & Nakagawa, Y. O. (2026) · Physical Review Research · doi:10.1103/dmn4-snfx · Cited by 162
Accelerated variational algorithms for digital quantum simulation of many-body ground states
Lyu, C., Montenegro, V., & Bayat, A. (2020) · Quantum · doi:10.22331/q-2020-09-16-324 · Cited by 27
Quantum locally linear embedding for nonlinear dimensionality reduction
He, X., Li, S., Lyu, C., & Wang, X. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-02818-y · Cited by 27
Mizuta, K., Fujii, M., Fujii, S., Ichikawa, K., Imamura, Y., Okuno, Y., & Nakagawa, Y. O. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043121 · Cited by 27
Chan, H. H. S., Fitzpatrick, N., Segarra‐Martí, J., Bearpark, M. J., & Tew, D. P. (2021) · Physical Chemistry Chemical Physics · doi:10.1039/d1cp02227j · Cited by 31
Variational quantum packaged deflation for arbitrary excited states
Wen, J., Lv, D., Yung, M., & Long, G. (2021) · Quantum Engineering · doi:10.1002/que2.80 · Cited by 27
Xie, Q., Liu, S., & Zhao, Y. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00159 · Cited by 27
Training Variational Quantum Circuits with CoVaR: Covariance Root Finding with Classical Shadows
Boyd, G., & Koczor, B. (2022) · Physical Review X · doi:10.1103/physrevx.12.041022 · Cited by 27
The barren plateaus of quantum neural networks: review, taxonomy and trends
Qi, H., Wang, L., Zhu, H., Gani, A., & Gong, C. (2023) · Quantum Information Processing · doi:10.1007/s11128-023-04188-7 · Cited by 31
Quantum Computer-Aided Design: Digital Quantum Simulation of Quantum Processors
Kyaw, T. H., Menke, T., Sim, S., Anand, A., Sawaya, N. P. D., Oliver, W. D., Guerreschi, G. G., & Aspuru‐Guzik, A. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.044042 · Cited by 27
Variational Quantum Simulation of Chemical Dynamics with Quantum Computers
Lee, C., Hsieh, C., Zhang, S., & Shi, L. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01176 · Cited by 28
Bierman, J., Li, Y., & Lu, J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00895 · Cited by 28
Optimal training of variational quantum algorithms without barren plateausPreprint
Haug, T., & Kim, M. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.14543 · Cited by 55
Quantum inverse iteration algorithm for programmable quantum simulators
Kyriienko, O. (2025) · Figshare · Cited by 27
Variational Quantum-Based Simulation of Waveguide Modes
Ewe, W.-B., Koh, D. E., Goh, S. T., Chu, H., & Png, C. E. (2022) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2022.3151510 · Cited by 31
One-particle Green's functions from the quantum equation of motion algorithm
Rizzo, J., Libbi, F., Tacchino, F., Ollitrault, P. J., Marzari, N., & Tavernelli, I. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043011 · Cited by 27
Two Algorithms for Excited-State Quantum Solvers: Theory and Application to EOM-UCCSD
Kim, Y., & Krylov, A. I. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c02480 · Cited by 25
Calculating nonadiabatic couplings and Berry's phase by variational quantum eigensolvers
Tamiya, S., Koh, S., & Nakagawa, Y. O. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.023244 · Cited by 22
Preparing valence-bond-solid states on noisy intermediate-scale quantum computers
Murta, B., Cruz, P. M. Q., & Fernández-Rossier, J. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013190 · Cited by 23
Quantum simulation of excited states from parallel contracted quantum eigensolvers
Benavides-Riveros, C. L., Wang, Y., Warren, S., & Mazziotti, D. A. (2024) · New Journal of Physics · doi:10.1088/1367-2630/ad2d1d · Cited by 25
Collective optimization for variational quantum eigensolvers
Zhang, D.-B., & Yin, T. (2020) · Physical Review A · doi:10.1103/physreva.101.032311 · Cited by 24
Quantum Measurement for Quantum Chemistry on a Quantum Computer
Patel, S., Jayakumar, P., Yen, T.-C., & Izmaylov, A. F. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00055 · Cited by 36
Quantum computing critical exponents
Dreyer, H., Bejan, M., & Granet, E. (2021) · Physical Review A · doi:10.1103/physreva.104.062614 · Cited by 22
Finite-size criticality in fully connected spin models on superconducting quantum hardware
Grossi, M., Kiss, O., Luca, F. D., Zollo, C., Gremese, I., & Mandarino, A. (2023) · Physical review. E · doi:10.1103/physreve.107.024113 · Cited by 23
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 24
Quantum Davidson algorithm for excited states
Tkachenko, N. V., Cincio, Ł., Boldyrev, A. I., Tretiak, S., Dub, P. A., & Zhang, Y. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad3a97 · Cited by 40
Post-Hartree–Fock method in quantum chemistry for quantum computer
Shikano, Y., Watanabe, H. C., Nakanishi, K. M., & Ohnishi, Y. (2021) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-021-00087-z · Cited by 22
A systematic variational approach to band theory in a quantum computer
Sherbert, K., Cerasoli, F., & Nardelli, M. B. (2021) · RSC Advances · doi:10.1039/d1ra07451b · Cited by 19
State Preparation Boosters for Early Fault-Tolerant Quantum Computation
Wang, G., Sim, S., & Johnson, P. D. (2022) · Quantum · doi:10.22331/q-2022-10-06-829 · Cited by 27
A diagrammatic approach to variational quantum ansatz construction
Herasymenko, Y., & O’Brien, T. E. (2021) · Quantum · doi:10.22331/q-2021-12-02-596 · Cited by 22
Sampling Overhead Analysis of Quantum Error Mitigation: Uncoded vs. Coded Systems
Xiong, Y., Chandra, D., Ng, S. X., & Hanzo, L. (2020) · IEEE Access · doi:10.1109/access.2020.3045016 · Cited by 22
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 23
Algorithmic Shadow Spectroscopy
Chan, H. H. S., Meister, R., Goh, M. L., & Koczor, B. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.010352 · Cited by 36
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
Quasiparton distributions in massive QED2: Toward quantum computation
Grieninger, S., Ikeda, K., & Ismail Zahed, A. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.076008 · Cited by 19
Variational simulation of Schwinger's Hamiltonian with polarization qubits
Borzenkova, O. V., Struchalin, G. I., Kardashin, A. S., Krasnikov, V. V., Skryabin, N. N., Straupe, S. S., Kulik, S. P., & Biamonte, J. (2021) · Applied Physics Letters · doi:10.1063/5.0043322 · Cited by 19
Kosugi, T., & Matsushita, Y. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033043 · Cited by 21
Quantum Computing for High-Energy Physics: State of the Art and ChallengesPreprint
Meglio, A. D., Jansen, K., Tavernelli, I., Alexandrou, C., Arunachalam, S., Bauer, C., Borras, K., Carrazza, S., Crippa, A., Croft, V. A., et al. (2023) · arXiv (Cornell University) · doi:10.3204/pubdb-2023-04595 · Cited by 24
GSQAS: Graph Self-supervised Quantum Architecture Search
He, Z., Deng, M., Zheng, S., Li, L., & Situ, H. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129286 · Cited by 22
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
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
Ground and Excited States from Ensemble Variational Principles
Ding, L., Hong, C.-L., & Schilling, C. (2024) · Quantum · doi:10.22331/q-2024-11-14-1525 · 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
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
Ensemble-learning error mitigation for variational quantum shallow-circuit classifiers
Li, Q., Huang, Y., Hou, X., Li, Y., Wang, X., & Bayat, A. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013027 · Cited by 22
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
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
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
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 23
Quantum algorithms for the generalized eigenvalue problem
Liang, J., Shen, S., Li, M., & Fei, S.-M. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03370-z · Cited by 16
Quantum simulation of two-dimensional quantum chemistry in optical lattices
Argüello-Luengo, J., González-Tudela, A., Shi, T., Zoller, P., & Cirac, J. I. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.042013 · Cited by 16
Metadata
- Authors
- Higgott, O., Wang, D., & Brierley, S.
- Affiliations
- Riverlane (United Kingdom) (GB)
- Venue
- Quantum
- Published
- 2019-07-01
- Volume
- 3
- Pages
- 156
- ISSN
- 2521-327X (ISSN-L)
- OpenAlex
- W2803208764
- Cited by
- 463
- References
- 44
- Citations by year
- 2026: 48 · 2025: 68 · 2024: 69 · 2023: 68 · 2022: 69 · 2021: 83 · 2020: 42 · 2019: 14
- FWCI
- 23.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Quantum (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- 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
- excitation energies, circuit depth, control error, error mitigation, near-term quantum devices
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
