- Open Access
Quantum-selected configuration interaction with time-evolved state
Phys. Rev. Research 7, 043043 – Published 14 October, 2025
DOI: https://doi.org/10.1103/75pv-hbrx
Abstract
Quantum-selected configuration interaction (QSCI) utilizes an input quantum state on a quantum device to select important bases (electron configurations in quantum chemistry) that define a subspace in which to diagonalize a target Hamiltonian, i.e., perform selected configuration interaction, on classical computers. Previous proposals for preparing a good input state, which is crucial for the quality of QSCI, based on optimization of quantum circuits may suffer from optimization difficulty and require many runs of the quantum device. Here, we propose using a time-evolved state by the target Hamiltonian (for some initial state) as an input of QSCI. Our proposal is based on the intuition that the time evolution by the Hamiltonian creates electron excitations of various orders when applied to the initial state. We numerically investigate the accuracy of the energy obtained by the proposed method for quantum chemistry Hamiltonians describing electronic states of small molecules. Numerical results reveal that our method can yield sufficiently accurate ground-state energies for the investigated molecules. Systematic analysis when increasing the number of qubits in a hydrogen chain shows that the subspace size required for sufficiently accurate results is reasonable at system sizes that cannot be solved by naive classical diagonalization. Our proposal provides a systematic and optimization-free method to prepare the input state of QSCI and could contribute to practical applications of quantum computers in quantum chemistry calculations.
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References (66)
- R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
- Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. D. Sawaya, S. Sim, L. Veis, and A. Aspuru-Guzik, Quantum chemistry in the age of quantum computing, Chem. Rev. 119, 10856 (2019).
- S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin, and X. Yuan, Quantum computational chemistry, Rev. Mod. Phys. 92, 015003 (2020).
- A. Y. Kitaev, Quantum measurements and the Abelian stabilizer problem, arXiv:quant-ph/9511026.
- R. Cleve, A. Ekert, C. Macchiavello, and M. Mosca, Quantum algorithms revisited, Proc. R. Soc. Lond. A 454, 339 (1998).
- A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O'Brien, A variational eigenvalue solver on a photonic quantum processor, Nat. Commun. 5, 4213 (2014).
- J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, and J. Tennyson, The variational quantum eigensolver: A review of methods and best practices, Phys. Rep. 986, 1 (2022).
- J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, Barren plateaus in quantum neural network training landscapes, Nat. Commun. 9, 4812 (2018).
- J. F. Gonthier, M. D. Radin, C. Buda, E. J. Doskocil, C. M. Abuan, and J. Romero, Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis, Phys. Rev. Res. 4, 033154 (2022).
- D. Layden, G. Mazzola, R. V. Mishmash, M. Motta, P. Wocjan, J.-S. Kim, and S. Sheldon, Quantum-enhanced Markov chain Monte Carlo, Nature (London) 619, 282 (2023).
- W. J. Huggins, B. A. O'Gorman, N. C. Rubin, D. R. Reichman, R. Babbush, and J. Lee, Unbiasing fermionic quantum Monte Carlo with a quantum computer, Nature (London) 603, 416 (2022).
- R. M. Parrish and P. L. McMahon, Quantum filter diagonalization: Quantum eigendecomposition without full quantum phase estimation, arXiv:1909.08925.
- N. H. Stair, R. Huang, and F. A. Evangelista, A multireference quantum Krylov algorithm for strongly correlated electrons, J. Chem. Theory Comput. 16, 2236 (2020).
- K. Kanno, M. Kohda, R. Imai, S. Koh, K. Mitarai, W. Mizukami, and Y. O. Nakagawa, Quantum-selected configuration interaction: Classical diagonalization of Hamiltonians in subspaces selected by quantum computers, arXiv:2302.11320.
- F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell et al., Quantum supremacy using a programmable superconducting processor, Nature (London) 574, 505 (2019).
- H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall, An adaptive variational algorithm for exact molecular simulations on a quantum computer, Nat. Commun. 10, 3007 (2019).
- Y. O. Nakagawa, M. Kamoshita, W. Mizukami, S. Sudo, and Y.-y. Ohnishi, ADAPT-QSCI: Adaptive construction of an input state for quantum-selected configuration interaction, J. Chem. Theory Comput. 20, 10817 (2024).
- J. Robledo-Moreno, M. Motta, H. Haas, A. Javadi-Abhari, P. Jurcevic, W. Kirby, S. Martiel, K. Sharma, S. Sharma, T. Shirakawa, I. Sitdikov, R.-Y. Sun, K. J. Sung, M. Takita, M. C. Tran, S. Yunoki, and A. Mezzacapo, Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer, Sci. Adv. 11, eadu9991 (2025).
- K. Sugisaki, S. Kanno, T. Itoko, R. Sakuma, and N. Yamamoto, Hamiltonian simulation-based quantum-selected configuration interaction for large-scale electronic structure calculations with a quantum computer, Phys. Chem. Chem. Phys. 27, 20869 (2025).
- P. Jordan and E. Wigner, Über das paulische äquivalenzverbot, Z. Angew. Phys. 47, 631 (1928).
- S. B. Bravyi and A. Y. Kitaev, Fermionic quantum computation, Ann. Phys. 298, 210 (2002).
- J. T. Seeley, M. J. Richard, and P. J. Love, The Bravyi-Kitaev transformation for quantum computation of electronic structure, J. Chem. Phys. 137, 224109 (2012).
- T. Helgaker, P. Jørgensen, and J. Olsen, Molecular Electronic- Structure Theory (John Wiley & Sons, West Sussex, 2000).
- C. F. Bender and E. R. Davidson, Studies in configuration interaction: The first-row diatomic hydrides, Phys. Rev. 183, 23 (1969).
- J. Whitten and M. Hackmeyer, Configuration interaction studies of ground and excited states of polyatomic molecules. I. The ci formulation and studies of formaldehyde, J. Chem. Phys. 51, 5584 (1969).
- B. Huron, J. Malrieu, and P. Rancurel, Iterative perturbation calculations of ground and excited state energies from multiconfigurational zeroth-order wavefunctions, J. Chem. Phys. 58, 5745 (1973).
- R. J. Buenker and S. D. Peyerimhoff, Individualized configuration selection in CI calculations with subsequent energy extrapolation, Theor. Chim. Acta 35, 33 (1974).
- R. J. Buenker and S. D. Peyerimhoff, Energy extrapolation in CI calculations, Theor. Chim. Acta 39, 217 (1975).
- H. Nakatsuji, Cluster expansion of the wavefunction, valence and Rydberg excitations, ionizations, and inner-valence ionizations of and studied by the sac and sac CI theories, Chem. Phys. 75, 425 (1983).
- R. Cimiraglia and M. Persico, Recent advances in multireference second order perturbation CI: The CIPSI method revisited, J. Comput. Chem. 8, 39 (1987).
- R. J. Harrison, Approximating full configuration interaction with selected configuration interaction and perturbation theory, J. Chem. Phys. 94, 5021 (1991).
- J. Greer, Estimating full configuration interaction limits from a Monte Carlo selection of the expansion space, J. Chem. Phys. 103, 1821 (1995).
- J. Greer, Monte Carlo configuration interaction, J. Comput. Phys. 146, 181 (1998).
- F. A. Evangelista, Adaptive multiconfigurational wave functions, J. Chem. Phys. 140, 124114 (2014).
- A. A. Holmes, H. J. Changlani, and C. Umrigar, Efficient heat-bath sampling in Fock space, J. Chem. Theory Comput. 12, 1561 (2016).
- J. B. Schriber and F. A. Evangelista, Communication: An adaptive configuration interaction approach for strongly correlated electrons with tunable accuracy, J. Chem. Phys. 144, 161106 (2016).
- A. A. Holmes, N. M. Tubman, and C. Umrigar, Heat-bath configuration interaction: An efficient selected configuration interaction algorithm inspired by heat-bath sampling, J. Chem. Theory Comput. 12, 3674 (2016).
- N. M. Tubman, J. Lee, T. Y. Takeshita, M. Head-Gordon, and K. B. Whaley, A deterministic alternative to the full configuration interaction quantum Monte Carlo method, J. Chem. Phys. 145, 044112 (2016).
- Y. Ohtsuka and J. Hasegawa, Selected configuration interaction using sampled first-order corrections to wave functions, J. Chem. Phys. 147, 034102 (2017).
- J. B. Schriber and F. A. Evangelista, Adaptive configuration interaction for computing challenging electronic excited states with tunable accuracy, J. Chem. Theory Comput. 13, 5354 (2017).
- S. Sharma, A. A. Holmes, G. Jeanmairet, A. Alavi, and C. J. Umrigar, Semistochastic heat-bath configuration interaction method: Selected configuration interaction with semistochastic perturbation theory, J. Chem. Theory Comput. 13, 1595 (2017).
- R. Chakraborty, P. Ghosh, and D. Ghosh, Evolutionary algorithm based configuration interaction approach, Int. J. Quantum Chem. 118, e25509 (2018).
- J. P. Coe, Machine learning configuration interaction, J. Chem. Theory Comput. 14, 5739 (2018).
- J. P. Coe, Machine learning configuration interaction for ab initio potential energy curves, J. Chem. Theory Comput. 15, 6179 (2019).
- V. Abraham and N. J. Mayhall, Selected configuration interaction in a basis of cluster state tensor products, J. Chem. Theory Comput. 16, 6098 (2020).
- N. M. Tubman, C. D. Freeman, D. S. Levine, D. Hait, M. Head-Gordon, and K. B. Whaley, Modern approaches to exact diagonalization and selected configuration interaction with the adaptive sampling CI method, J. Chem. Theory Comput. 16, 2139 (2020).
- N. Zhang, W. Liu, and M. R. Hoffmann, Iterative configuration interaction with selection, J. Chem. Theory Comput. 16, 2296 (2020).
- N. Zhang, W. Liu, and M. R. Hoffmann, Further development of iCIPT2 for strongly correlated electrons, J. Chem. Theory Comput. 17, 949 (2021).
- V. G. Chilkuri and F. Neese, Comparison of many-particle representations for selected-CI I: A tree based approach, J. Comput. Chem. 42, 982 (2021).
- V. G. Chilkuri and F. Neese, Comparison of many-particle representations for selected configuration interaction: II. Numerical benchmark calculations, J. Chem. Theory Comput. 17, 2868 (2021).
- J. J. Goings, H. Hu, C. Yang, and X. Li, Reinforcement learning configuration interaction, J. Chem. Theory Comput. 17, 5482 (2021).
- S. D. Pineda Flores, Chembot: A machine learning approach to selective configuration interaction, J. Chem. Theory Comput. 17, 4028 (2021).
- W. Jeong, C. A. Gaggioli, and L. Gagliardi, Active learning configuration interaction for excited-state calculations of polycyclic aromatic hydrocarbons, J. Chem. Theory Comput. 17, 7518 (2021).
- K. Seth and D. Ghosh, Active learning assisted MCCI to target spin states, J. Chem. Theory Comput. 19, 524 (2023).
- NIST Computational Chemistry Comparison and Benchmark Database, NIST Standard Reference Database Number 101 (2022), https://cccbdb.nist.gov/.
- Q. Sun, T. C. Berkelbach, N. S. Blunt, G. H. Booth, S. Guo, Z. Li, J. Liu, J. D. McClain, E. R. Sayfutyarova, S. Sharma, S. Wouters, and G. K.-L. Chan, PySCF: The python-based simulations of chemistry framework, WIREs Comput. Mol. Sci. 8, e1340 (2018).
- Q. Sun et al., Recent developments in the pyscf program package, J. Chem. Phys. 153, 024109 (2020).
- J. R. McClean et al., OpenFermion: The electronic structure package for quantum computers, Quantum Sci. Technol. 5, 034014 (2020).
- Y. Suzuki, Y. Kawase, Y. Masumura, Y. Hiraga, M. Nakadai, J. Chen, K. M. Nakanishi, K. Mitarai, R. Imai, S. Tamiya, T. Yamamoto, T. Yan, T. Kawakubo, Y. O. Nakagawa, Y. Ibe, Y. Zhang, H. Yamashita, H. Yoshimura, A. Hayashi, and K. Fujii, Qulacs: A fast and versatile quantum circuit simulator for research purpose, Quantum 5, 559 (2021).
- A. Anand, P. Schleich, S. Alperin-Lea, P. W. K. Jensen, S. Sim, M. Díaz-Tinoco, J. S. Kottmann, M. Degroote, A. F. Izmaylov, and A. Aspuru-Guzik, A quantum computing view on unitary coupled cluster theory, Chem. Soc. Rev. 51, 1659 (2022).
- QURI parts, 2022, https://github.com/QunaSys/quri-parts.
- M. R. Hirsbrunner, D. Chamaki, J. W. Mullinax, and N. M. Tubman, Beyond MP2 initialization for unitary coupled cluster quantum circuits, Quantum 8, 1538 (2024).
- Y. Matsuzawa and Y. Kurashige, Jastrow-type decomposition in quantum chemistry for low-depth quantum circuits, J. Chem. Theory Comput. 16, 944 (2020).
- M. Motta, K. J. Sung, K. B. Whaley, M. Head-Gordon, and J. Shee, Bridging physical intuition and hardware efficiency for correlated electronic states: The local unitary cluster Jastrow ansatz for electronic structure, Chem. Sci. 14, 11213 (2023).
- Y. Akahoshi, K. Maruyama, H. Oshima, S. Sato, and K. Fujii, Partially fault-tolerant quantum computing architecture with error-corrected Clifford gates and space-time efficient analog rotations, PRX Quantum 5, 010337 (2024).
- E. Campbell, Random compiler for fast Hamiltonian simulation, Phys. Rev. Lett. 123, 070503 (2019).