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 201–300 of 463
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
Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems
Liang, J., Wei, S.-J., & Fei, S.-M. (2022) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-021-1844-7 · Cited by 16
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
Efficient assessment of process fidelity
Greenaway, S., Sauvage, F., Khosla, K. E., & Mintert, F. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033031 · Cited by 15
Variational quantum simulation of thermal statistical states on a superconducting quantum processer
Guo, X., Li, S.-S., Xiao, X., Xiang, Z., Ge, Z.-Y., Li, H., Song, P., Peng, Y., Wang, Z., Xu, K., et al. (2022) · Chinese Physics B · doi:10.1088/1674-1056/aca7f3 · Cited by 23
Parrish, R. M., Hohenstein, E. G., McMahon, P. L., & Martı́nez, T. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08728 · Cited by 18
Multilevel variational spectroscopy using a programmable quantum simulator
Han, Z., Lyu, C., Zhou, Y., Yuan, J., Chu, J., Nuerbolati, W., Jia, H. W., Nie, L., Wei, W., Yang, Z., et al. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013015 · Cited by 14
Subspace Methods for the Simulation of Molecular Response Properties on a Quantum Computer
Reinholdt, P., Kjellgren, E. R., Fuglsbjerg, J. H., Ziems, K. M., Coriani, S., Sauer, S. P. A., & Kongsted, J. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00211 · Cited by 15
A general framework for active space embedding methods with applications in quantum computing
Battaglia, S., Rossmannek, M., Rybkin, V. V., Tavernelli, I., & Hutter, J. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01477-2 · Cited by 20
From Digital Humanities to Quantum Humanities: Potentials and Applications
Barzen, J. (2022) · Springer eBooks · doi:10.1007/978-3-030-95538-0_1 · Cited by 17
Quantum correlation alignment for unsupervised domain adaptation
He, X. (2020) · Physical Review A · doi:10.1103/physreva.102.032410 · Cited by 15
Quantum computing Floquet energy spectra
Fauseweh, B., & Zhu, J. (2023) · Quantum · doi:10.22331/q-2023-07-20-1063 · Cited by 22
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 19
Bruschi, M., Gallina, F., & Fresch, B. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c03499 · Cited by 13
Marti-Dafcik, D., Burton, H. G. A., & Tew, D. P. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013191 · Cited by 20
Hybrid quantum-classical algorithms for solving quantum chemistry in Hamiltonian–wave-function space
Yuan, Z.-H., Yin, T., & Zhang, D.-B. (2021) · Physical Review A · doi:10.1103/physreva.103.012413 · Cited by 13
Entanglement diagnostics for efficient VQA optimization
Kim, J., & Oz, Y. (2022) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ac7791 · Cited by 13
Beaujeault-Taudière, Y., & Denis Lacroix, A. (2024) · Physical Review C · doi:10.1103/physrevc.109.024327 · Cited by 15
Quantum computation of eigenvalues within target intervals
Jensen, P. W. K., Kristensen, L. B., Kottmann, J. S., & Aspuru-Guzik, A. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abc096 · Cited by 13
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 19
Partitioning Quantum Chemistry Simulations with Clifford Circuits
Schleich, P., Boen, J., Cincio, Ł., Anand, A., Kottmann, J. S., Tretiak, S., Dub, P. A., & Aspuru‐Guzik, A. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00335 · Cited by 17
Nonadiabatic Molecular Dynamics with Fermionic Subspace-Expansion Algorithms on Quantum Computers
Gandon, A., Baiardi, A., Ollitrault, P. J., & Tavernelli, I. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00233 · Cited by 14
Search space pruning for quantum architecture search
He, Z., Su, J., Chen, C., Pan, M., & Situ, H. (2022) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-022-02714-7 · Cited by 12
Analog quantum approximate optimization algorithm
Barraza, N., Barrios, G. A., Peng, J., Lamata, L., Solano, E., & Albarrán-Arriagada, F. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac91f0 · Cited by 12
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
Evaluation of vibrational energies and wave functions of CO2 on a quantum computer
Lötstedt, E., Yamanouchi, K., & Tachikawa, Y. (2022) · AVS Quantum Science · doi:10.1116/5.0091144 · Cited by 11
Nguyen, M. T., Lee, Y., Alfonso, D. R., Shao, Q., & Duan, Y. (2023) · AVS Quantum Science · doi:10.1116/5.0137750 · Cited by 14
Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor
Lau, J. W. Z., Lim, K. H., Bharti, K., Kwek, L. C., & Vinjanampathy, S. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.240601 · Cited by 12
Implementation of the Projective Quantum Eigensolver on a Quantum Computer
Misiewicz, J. P., & Evangelista, F. A. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c07429 · Cited by 11
Maximally localized dynamical quantum embedding for solving many-body correlated systems
Lupo, C., Jamet, F., Tse, W. H. T., Rungger, I., & Weber, C. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00090-3 · Cited by 11
Quantum computation for predicting electron and phonon properties of solids
Choudhary, K. (2021) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/ac1154 · Cited by 11
Verma, S., Mitra, A., Wang, Q., D’Cunha, R., Jangid, B., Hennefarth, M. R., Agarawal, V., Otis, L., Haldar, S., Hermes, M. R., et al. (2026) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00486 · Cited by 16
Carpentier, P., Pillet, N., Denis Lacroix, A., Dubray, N., & Regnier, D. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.152501 · Cited by 10
Gil, E. S., Oppel, M., Kottmann, J. S., & González, L. (2024) · Chemical Science · doi:10.1039/d4sc04987j · Cited by 12
Alfonso, D. R., Lee, Y., Paudel, H. P., & Duan, Y. (2025) · Applied Physics Reviews · doi:10.1063/5.0245874 · Cited by 11
A Quantum-Computing-Based Method for Solving Quantum Confinement Problem in Semiconductor
Yang, N., & Guo, J. H. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3234887 · Cited by 10
Sato, Y., Watanabe, H. C., Raymond, R., Kondo, R., Wada, K., Endo, K., Sugawara, M., & Yamamoto, N. (2023) · Physical Review A · doi:10.1103/physreva.108.022429 · Cited by 10
Near-Term Efficient Quantum Algorithms for Entanglement Analysis
Chen, R., Zhao, B., & Wang, X. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.024071 · Cited by 13
Many-Body Excited States with a Contracted Quantum Eigensolver
Smart, S. E., Welakuh, D. M., & Narang, P. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00030 · Cited by 13
Yalouz, S., Senjean, B., Miatto, F. M., & Dunjko, V. (2021) · Quantum · doi:10.22331/q-2021-11-08-572 · Cited by 13
Ensemble variational Monte Carlo for optimization of correlated excited state wave functions
Wheeler, W. A., Kleiner, K. G., & Wagner, L. K. (2024) · Electronic Structure · doi:10.1088/2516-1075/ad38f8 · Cited by 9
Reduced Density Matrix Formulation of Quantum Linear Response
von Buchwald, T. J., Ziems, K. M., Kjellgren, E. R., Sauer, S. P. A., Kongsted, J., & Coriani, S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00574 · Cited by 9
Singh, H., Majumder, S., & Mishra, S. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0210597 · Cited by 9
Quantum computing for extracting nuclear resonances
Zhang, H., Bai, D., & Ren, Z. (2024) · Physics Letters B · doi:10.1016/j.physletb.2024.139187 · Cited by 11
Yoshikawa, T., Takanashi, T., & Nakai, H. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00602 · Cited by 10
Fast-forwarding quantum simulation with real-time quantum Krylov subspace algorithms
Cortes, C. L., DePrince, A. E., & Gray, S. K. (2022) · Physical Review A · doi:10.1103/physreva.106.042409 · Cited by 12
A full circuit-based quantum algorithm for excited-states in quantum chemistry
Wen, J., Wang, Z., Chen, C.-T., Xiao, J., Li, H., Qian, L., Huang, Z., Fan, H., Wei, S., & Long, G. (2024) · Quantum · doi:10.22331/q-2024-01-04-1219 · Cited by 13
Quantum computing for classical problems: variational quantum eigensolver for activated processes
Pravatto, P., Castaldo, D., Gallina, F., Fresch, B., Corni, S., & Moro, G. J. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac3ff9 · Cited by 11
Differentiable quantum computational chemistry with PennyLanePreprint
Arrazola, J. M., Jahangiri, S., Delgado, A., Ceroni, J., Izaac, J., Száva, A., Azad, U., Lang, R. A., Niu, Z., Matteo, O. D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.09967 · Cited by 37
Noise robust detection of quantum phase transitions
Lively, K., Bode, T., Szangolies, J., Zhu, J., & Fauseweh, B. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043254 · Cited by 9
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 11
Singh, N., Siwach, P., & Arumugam, P. (2025) · Physical Review C · doi:10.1103/bbkf-fjxj · Cited by 12
Variational Quantum Computing for Quantum Simulation: Principles, Implementations, and Challenges
Galvão, L. Q., de Souza, A. B. M., Moret, M. A., & Cruz, C. (2025) · Brazilian Journal of Physics · doi:10.1007/s13538-025-01946-z · Cited by 8
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 11
Quantum Computation of Conical Intersections on a Programmable Superconducting Quantum Processor
Zhao, S., Tang, D., Xiao, X., Wang, R., Sun, Q., Chen, Z., Cai, X., Li, Z., Yu, H., & Fang, W. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01314 · Cited by 11
Measuring Trotter error and its application to precision-guaranteed Hamiltonian simulations
Ikeda, T. N., Kono, H., & Fujii, K. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033285 · Cited by 10
Shirai, S., Horiba, T., & Hirai, H. (2022) · ACS Omega · doi:10.1021/acsomega.2c01053 · Cited by 8
Sajjan, M., Alaeian, H., & Kais, S. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0128283 · Cited by 8
Concurrent quantum eigensolver for multiple low-energy eigenstates
Xu, G., Guo, Y., Li, X., Wang, K., Fan, Z., Zhou, Z., Liao, H., & Xiang, T. (2023) · Physical Review A · doi:10.1103/physreva.107.052423 · Cited by 9
A method of determining molecular excited-states using quantum computation
Jouzdani, P., Bringuier, S. A., & Kostuk, M. (2021) · MRS Advances · doi:10.48550/arxiv.1908.05238 · Cited by 13
Subspace-Search Quantum Imaginary Time Evolution for Excited State Computations
Cianci, C., dos Santos, L. F., & Batista, V. S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00915 · Cited by 10
Singh, H., Majumder, S., & Mishra, S. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0249447 · Cited by 7
Quantum simulations of nuclear resonances with variational methods
Singh, A., Siwach, P., & Arumugam, P. (2025) · Physical Review C · doi:10.1103/x8vx-y5mh · Cited by 10
Shirai, S., Tseng, S.-Y., Iwakiri, H., Horiba, T., Hirai, H., & Koh, S. (2025) · ACS Omega · doi:10.1021/acsomega.5c03371 · Cited by 10
Li, S. X., Mu, W. L., You, J.-B., & Shao, X. Q. (2024) · Physical Review A · doi:10.1103/physreva.109.062603 · Cited by 9
Quantum Equation-of-Motion Method with Single, Double, and Triple Excitations
Zheng, Y., Sun, Z., Liu, J., Fan, Y., Li, Z., & Yang, J. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01071 · Cited by 8
Yoshikura, T., Ten‐no, S., & Tsuchimochi, T. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c02800 · Cited by 9
Multi-state quantum simulations via model-space quantum imaginary time evolution
Tsuchimochi, T., Ryo, Y., Tsang, S. C., & Ten‐no, S. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00780-y · Cited by 6
A universal variational quantum eigensolver for non-Hermitian systems
Zhao, H., Zhang, P., & Wei, T.-C. (2023) · Scientific Reports · doi:10.1038/s41598-023-49662-5 · Cited by 7
Hirai, H., & Koh, S. (2022) · Chemical Physics · doi:10.1016/j.chemphys.2022.111460 · Cited by 8
Yao, Q., Ji, Q., Li, X., Zhang, Y., Chen, X., Ju, M., Liu, J., & Wang, J. (2024) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.4c01445 · Cited by 7
Bae, J., Yoo, G., Nakamura, S., Ohnishi, S., & Kim, D. S. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04458-y · Cited by 7
Ďuriška, M., Miháliková, I., & Friák, M. (2025) · Physica Scripta · doi:10.1088/1402-4896/adbb29 · Cited by 8
A progressive predictor-based quantum architecture search with active learning
Deng, M., He, Z., Zheng, S., Zhou, Y., Zhang, F., & Situ, H. (2023) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-023-04537-6 · Cited by 6
Maximizing quantum-computing expressive power through randomized circuits
Yang, Y., Zhang, Z., Wang, A., Xu, X., Wang, X., & Li, Y. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.023098 · Cited by 8
Block Lanczos method for excited states on a quantum computer
Baker, T. E. (2024) · Physical Review A · doi:10.1103/physreva.110.012420 · Cited by 12
Ab initio extended Hubbard model of short polyenes for efficient quantum computing
Yoshida, Y., Takemori, N., & Mizukami, W. (2024) · The Journal of Chemical Physics · doi:10.1063/5.0213525 · Cited by 6
Characterizing Conical Intersections of Nucleobases on Quantum Computers
Wang, Y., Cianci, C., Avdic, I., Dutta, R., Warren, S., Allen, B., Vu, N. P., dos Santos, L. F., Batista, V. S., & Mazziotti, D. A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01434 · Cited by 9
Variational quantum simulation of long-range interacting systems
Lyu, C., Tang, X., Li, J., Xu, X., Yung, M., & Bayat, A. (2023) · New Journal of Physics · doi:10.1088/1367-2630/acd571 · Cited by 8
Mejía, L., Jia, Y., Reichman, D. R., Baer, R., Yang, C., & Rabani, E. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00296 · Cited by 10
Chemically Accurate Potential Curves for H2 Molecules Using Explicitly Correlated Qubit-ADAPT
Volkmann, H., Sathyanarayanan, R., Sáenz, A., Jansen, K., & Kühn, S. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01281 · Cited by 6
Silva, A. C. D. N., Galvão, L. Q., & Cruz, C. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad6ec3 · Cited by 6
Excited State Search Using Quantum Annealing
Seki, Y., Matsuzaki, Y., & Kawabata, S. (2021) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.90.054002 · Cited by 7
Power of the Sine Hamiltonian Operator for Estimating the Eigenstate Energies on Quantum Computers
Xie, Q., Song, Y., & Zhao, Y. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00759 · Cited by 6
Universal noise-precision relations in variational quantum algorithms
Ito, K., Mizukami, W., & Fujii, K. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.023025 · Cited by 9
Asnaashari, K., Bondarenko, D., & Krems, R. V. (2026) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp02860d · Cited by 7
Quantum computing of the pairing Hamiltonian at finite temperature
Jiang, C., & Pei, J. (2023) · Physical Review C · doi:10.1103/physrevc.107.044308 · Cited by 5
Simulating a quasiparticle on a quantum device
Jaiswal, R., Lovas, I., & Balents, L. (2025) · Physical Review A · doi:10.1103/physreva.111.012629 · Cited by 5
Variational optimization for quantum problems using deep generative networks
Zhang, L., Lin, X., Wang, P., Yang, K., Zeng, X., Wei, Z., & Wang, Z. (2025) · Communications Physics · doi:10.1038/s42005-025-02261-4 · Cited by 6
Quantum architecture search with neural predictor based on ZX-calculus
Li, S., Tsukayama, D., Shirakashi, J., Shibuya, T., & Imai, H. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00410-w · Cited by 6
Bierman, J., Li, Y., & Lu, J. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c01297 · Cited by 6
Full band-structure calculation of semiconducting materials on a noisy quantum processor
Zhang, S., Karim, A., Quiney, H. M., & Usman, M. (2024) · Physical Review A · doi:10.1103/physreva.110.062415 · Cited by 6
Hamiltonian forging of a thermofield double
Faílde, D., Santos-Suárez, J., Herrera-Martí, D. A., & Mas, J. (2025) · Physical Review A · doi:10.1103/physreva.111.012432 · Cited by 7
Maiti, S., Banerjee, D., Chakraborty, B., & Huffman, E. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013283 · Cited by 11
Dynamical mean field theory for real materials on a quantum computer
Selisko, J., Amsler, M., Wever, C., Kawashima, Y., Samsonidze, G. G., Haq, R. U., Tacchino, F., Tavernelli, I., & Eckl, T. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01772-6 · Cited by 19
Xie, Q., Zhang, W., Xu, X., Liu, S., & Zhao, Y. (2022) · International Journal of Quantum Chemistry · doi:10.1002/qua.27001 · Cited by 6
New Directions in Quantum Music: Concepts for a Quantum Keyboard and the Sound of the Ising Model
Clemente, G., Crippa, A., Jansen, K., & Tüysüz, C. (2022) · Springer eBooks · doi:10.1007/978-3-031-13909-3_17 · Cited by 5
Simulation of a Three‐Nucleons System Transition on Quantum Circuits
Nigro, L., Barbieri, C., & Prati, E. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202400371 · Cited by 5
Quantum algorithm for the calculation of transition amplitudes in hybrid tensor networks
Kanno, S., Endo, S., Suzuki, Y., & Tokunaga, Y. (2021) · Physical Review A · doi:10.1103/physreva.104.042424 · Cited by 5
Variational Quantum Eigensolver with Mutual Variance-Hamiltonian Optimization
Chen, B.-L., & Zhang, D.-B. (2022) · Chinese Physics Letters · doi:10.1088/0256-307x/40/1/010303 · Cited by 5
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
