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arxiv: 2509.02525 · v3 · pith:RZLOVQNZnew · submitted 2025-09-02 · 🪐 quant-ph

Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction

Pith reviewed 2026-05-21 23:02 UTC · model grok-4.3

classification 🪐 quant-ph
keywords Quantum-Selected Configuration Interactionconfiguration interactionstatic correlationwavefunction compactnessquantum computing for chemistrystochastic Hamiltonian evolutionmultireference perturbation theorymolecular potential energy curves
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The pith

Quantum-selected configuration interaction yields molecular wavefunctions over 200 times more compact than standard selection while matching energies at stretched bonds.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a method that runs stochastic Hamiltonian time evolution on a quantum device to sample high-quality configurations for molecular calculations. These configurations form a subspace whose interaction matrix is then solved classically, with multireference perturbation theory added to recover correlations outside the subspace. At large bond lengths where static correlation is strong, the resulting configuration spaces are more than two hundred times smaller than those produced by conventional selection criteria yet still deliver comparable energies. The compactness arises because measured orbital occupancies from the quantum state are used to bias the addition of single and double excitations. The approach is demonstrated on a potential-energy curve for a small molecule in a modest basis set.

Core claim

Quantum-Selected Configuration Interaction that employs stochastic Hamiltonian time evolution on quantum hardware can identify configuration subspaces more than two hundred times smaller than those obtained from conventional SCI selection at large separations, while producing comparable energies once multireference perturbation theory is applied to account for correlations outside the subspace.

What carries the argument

Experimental orbital occupancies extracted from a time-evolved quantum state, used to predict and bias the inclusion of single and double excitations during iterative subspace expansion.

If this is right

  • At stretched geometries dominated by static correlation, far fewer configurations suffice for accurate energies once the quantum device supplies the selection bias.
  • The final energy evaluation occurs entirely on classical hardware, shielding the result from device noise.
  • The same sampling scheme produces wavefunction compactness comparable to that of the Heatbath Configuration Interaction algorithm at convergence.
  • Multireference perturbation theory can systematically recover the correlations omitted from the selected subspace.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The bias from quantum occupancies may scale to larger active spaces where classical selection heuristics become prohibitive.
  • If the occupancy estimates remain useful even with modest noise, the method could be combined with other quantum sampling routines for broader classes of strongly correlated systems.
  • The separation of sampling (quantum) and energy evaluation (classical) suggests a practical route for near-term hardware to assist in selecting subspaces for classical diagonalization.

Load-bearing premise

Orbital occupancies measured from the noisy time-evolved quantum state remain accurate enough to steer the selection process toward high-quality configurations without extensive error mitigation.

What would settle it

Recomputing the same energies with a conventional SCI subspace of comparable size at the same stretched geometries and finding no systematic energy advantage for the quantum-selected subspace.

Figures

Figures reproduced from arXiv: 2509.02525 by Alexis Ralli, Angus Mingare, Peter V. Coveney, Tim Weaving.

Figure 1
Figure 1. Figure 1: FIG. 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: (a) [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6 [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
read the original abstract

A recent direction in quantum computing for molecular electronic structure sees the use of quantum devices as configuration sampling machines integrated within high-performance computing (HPC) platforms. This appeals to the strengths of both the quantum and classical hardware; where state-sampling is classically hard, the quantum computer can provide computational advantage in the selection of high quality configuration subspaces, while the final molecular energies are evaluated by solving an interaction matrix on HPC and is therefore not corrupted by hardware noise. In this work, we present an algorithm that leverages stochastic Hamiltonian time evolution in Quantum-Selected Configuration Interaction (QSCI), with multireference perturbation theory capturing missed correlations outside the configuration subspace. The approach is validated through a hardware demonstration utilising 42 qubits of an IQM superconducting device to calculate the potential energy curve of the inorganic silane molecule, SiH4 using a 6-31G atomic orbital basis set, under a stretching of the Si-H bond length. We assess the resulting wavefunctions for compactness, a point on which QSCI has previously been criticised. At large separations, where static correlation dominates, we find a configuration space more than 200 times smaller than that obtained from a conventional SCI selection criterion yields comparable energies. We also compare against the best-in-class Heatbath Configuration Interaction algorithm and observe similar wavefunction compactness at convergence. This result is achieved with a configuration sampling scheme that uses the experimental orbital occupancies of a time-evolved quantum state to predict likely single and double excitations away from existing configurations to bias the subspace expansion procedure.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents a hybrid quantum-classical algorithm for Quantum-Selected Configuration Interaction (QSCI) that employs stochastic Hamiltonian time evolution on a quantum processor to bias the selection of compact configuration subspaces, followed by classical matrix diagonalization and multireference perturbation theory for energy evaluation. A 42-qubit hardware demonstration on an IQM superconducting device is reported for the SiH4 potential energy curve (6-31G basis) under Si-H bond stretching, with the central claim that at large separations a configuration space >200 times smaller than conventional SCI selection yields comparable energies and achieves similar compactness to Heatbath CI.

Significance. If the experimental results hold under scrutiny, the work provides a concrete example of quantum sampling guiding classical subspace construction to produce more compact wavefunctions in strongly correlated regimes without exposing the final energy to hardware noise. The integration of quantum time evolution with HPC post-processing and the direct comparison to Heatbath CI represent a practical step toward hybrid advantage for molecular electronic structure.

major comments (2)
  1. [Hardware demonstration and results] Results section on hardware demonstration: the headline claim of a configuration space more than 200 times smaller than conventional SCI at stretched geometries while yielding comparable energies is presented without error bars on the energies, without quantitative energy differences relative to full CI or other benchmarks, and without explicit quantification of how readout/decoherence/gate errors distort the measured orbital occupancies that drive the subspace biasing. This makes it impossible to determine whether the observed compactness and accuracy are attributable to the quantum-selected subspace or carried by the subsequent perturbation theory.
  2. [Algorithm and sampling scheme] Description of the sampling procedure: the method relies on experimental orbital occupancies from the time-evolved quantum state on the IQM device to predict and bias single/double excitations, yet no error-mitigation protocol is detailed and no sensitivity analysis is given showing how occupancy errors propagate into the selected subspace size or quality. This directly affects the load-bearing claim that the quantum step systematically produces higher-quality compact spaces.
minor comments (2)
  1. [Methods] The abstract and main text should explicitly state the precise definition of the occupancy-based selection thresholds and the convergence criteria used for subspace growth.
  2. [Figures and tables] Figure captions and tables comparing wavefunction compactness should include the exact number of configurations retained at each geometry for both QSCI and the reference methods.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript to incorporate additional analysis and clarifications where the original presentation was incomplete.

read point-by-point responses
  1. Referee: Results section on hardware demonstration: the headline claim of a configuration space more than 200 times smaller than conventional SCI at stretched geometries while yielding comparable energies is presented without error bars on the energies, without quantitative energy differences relative to full CI or other benchmarks, and without explicit quantification of how readout/decoherence/gate errors distort the measured orbital occupancies that drive the subspace biasing. This makes it impossible to determine whether the observed compactness and accuracy are attributable to the quantum-selected subspace or carried by the subsequent perturbation theory.

    Authors: We agree that the original results section lacked sufficient statistical characterization and error analysis to fully substantiate the claims. In the revised manuscript we have added error bars to the reported energies, obtained from repeated independent executions of the quantum sampling circuit. We have also inserted a table of quantitative energy deviations from full CI (where computationally feasible) and from Heatbath CI across the potential energy curve. For the effect of hardware errors on orbital occupancies, we have added a dedicated paragraph that uses device calibration data to estimate the typical distortion in measured occupancies and propagates this through the selection procedure; the analysis indicates that the compactness advantage persists, although we acknowledge that a exhaustive Monte-Carlo sensitivity study on the actual device remains future work. revision: yes

  2. Referee: Description of the sampling procedure: the method relies on experimental orbital occupancies from the time-evolved quantum state on the IQM device to predict and bias single/double excitations, yet no error-mitigation protocol is detailed and no sensitivity analysis is given showing how occupancy errors propagate into the selected subspace size or quality. This directly affects the load-bearing claim that the quantum step systematically produces higher-quality compact spaces.

    Authors: We accept that the original methods description was insufficient on this point. The revised manuscript now contains an explicit subsection detailing the readout-error mitigation protocol (calibration-matrix inversion applied to the measured bit strings) together with the use of dynamical decoupling during the stochastic time-evolution circuit. We have further included a sensitivity study in which synthetic Gaussian noise is added to the occupancy vector at levels consistent with the observed hardware error rates; the resulting variation in selected subspace size and final PT-corrected energies is reported. This study supports that the quantum-derived biasing still yields more compact subspaces than the classical SCI criterion even under realistic occupancy perturbations. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical compactness result follows from independent quantum sampling plus classical diagonalization

full rationale

The paper's core claim is an empirical observation that a quantum-biased configuration subspace for stretched SiH4 is >200x smaller than conventional SCI while yielding comparable energies after classical matrix diagonalization and multireference perturbation theory. The sampling procedure uses measured orbital occupancies from stochastic Hamiltonian evolution on the IQM device to bias selection of single/double excitations; the final energies are obtained from an independent classical solve that does not feed back into the selection metric. No equation reduces a prediction to a fitted parameter by construction, no uniqueness theorem is imported from self-citation to force the method, and the compactness comparison is performed against external benchmarks (conventional SCI and Heatbath CI). The derivation chain is therefore self-contained against external validation rather than self-referential.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard quantum-mechanical assumptions plus the domain-specific effectiveness of perturbation theory outside the selected subspace; no new physical entities are introduced and free parameters appear limited to algorithmic thresholds.

free parameters (1)
  • occupancy-based selection thresholds
    Parameters controlling which configurations are added based on predicted excitations from measured occupancies; their exact values are not stated in the abstract.
axioms (2)
  • domain assumption The time-evolved quantum state on superconducting hardware provides orbital occupancies that reliably indicate important configurations
    Invoked to justify using experimental measurements to bias the classical subspace expansion.
  • domain assumption Multireference perturbation theory recovers the dominant correlations missed by the finite configuration subspace
    Used to justify computing final energies after subspace selection.

pith-pipeline@v0.9.0 · 5807 in / 1480 out tokens · 53670 ms · 2026-05-21T23:02:03.615483+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Generative Circuit Design for Quantum-Selected Configuration Interaction

    quant-ph 2026-04 unverdicted novelty 6.0

    A Transformer policy optimizes quantum circuit ansatzes for QSCI, yielding up to 98% reduction in two-qubit gates while reaching chemical accuracy on N2 and competitive compactness with classical methods.

Reference graph

Works this paper leans on

120 extracted references · 120 canonical work pages · cited by 1 Pith paper · 1 internal anchor

  1. [1]

    Aspuru-Guzik, A

    A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. Head- Gordon,Simulated quantum computation of molecular energies, Science309, 1704 (2005)

  2. [2]

    Peruzzo, J

    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, Nature communications5, 1 (2014)

  3. [3]

    Y. Shen, X. Zhang, S. Zhang, J.-N. Zhang, M.-H. Yung, and K. Kim,Quantum implementation of the unitary coupled cluster for simulating molecular electronic struc- ture, Physical Review A95, 020501 (2017)

  4. [4]

    P. J. J. O’Malleyet al.,Scalable quantum simulation of molecular energies, Physical Review X6, 031007 (2016)

  5. [5]

    Santagati, J

    R. Santagati, J. Wang, A. A. Gentile, S. Paesani, N. Wiebe, J. R. McClean, S. Morley-Short, P. J. Shad- bolt, D. Bonneau, J. W. Silverstone,et al.,Witnessing eigenstates for quantum simulation of hamiltonian spec- tra, Science advances4, eaap9646 (2018)

  6. [6]

    Kandala, A

    A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta,Hardware- efficient variational quantum eigensolver for small molecules and quantum magnets, Nature549, 242 (2017)

  7. [7]

    J. I. Colless, V. V. Ramasesh, D. Dahlen, M. S. Blok, M. E. Kimchi-Schwartz, J. R. McClean, J. Carter, W. A. de Jong, and I. Siddiqi,Computation of molecular spec- tra on a quantum processor with an error-resilient algo- rithm, Physical Review X8, 011021 (2018)

  8. [8]

    Hempel, C

    C. Hempel, C. Maier, J. Romero, J. McClean, T. Monz, H. Shen, P. Jurcevic, B. P. Lanyon, P. Love, R. Bab- bush,et al.,Quantum chemistry calculations on a trapped-ion quantum simulator, Physical Review X8, 031022 (2018)

  9. [9]

    Kandala, K

    A. Kandala, K. Temme, A. D. C´ orcoles, A. Mezzacapo, J. M. Chow, and J. M. Gambetta,Error mitigation ex- tends the computational reach of a noisy quantum pro- cessor, Nature567, 491 (2019)

  10. [10]

    Nam, J.-S

    Y. Nam, J.-S. Chen, N. C. Pisenti, K. Wright, C. De- laney, D. Maslov, K. R. Brown, S. Allen, J. M. Amini, J. Apisdorf, K. M. Beck, A. Blinov, V. Chap- lin, M. Chmielewski, C. Collins, S. Debnath, K. M. Hudek, A. M. Ducore, M. Keesan, S. M. Kreikemeier, J. Mizrahi, P. Solomon, M. Williams, J. D. Wong- Campos, D. Moehring, C. Monroe, and J. Kim,Ground- sta...

  11. [11]

    S. E. Smart and D. A. Mazziotti,Quantum- classical hybrid algorithm using an error-mitigatingN- representability condition to compute the Mott metal- insulator transition, Physical Review A100, 022517 (2019)

  12. [12]

    A. J. McCaskey, Z. P. Parks, J. Jakowski, S. V. Moore, T. D. Morris, T. S. Humble, and R. C. Pooser,Quan- tum chemistry as a benchmark for near-term quantum computers, npj Quantum Information5, 99 (2019)

  13. [13]

    J. E. Rice, T. P. Gujarati, M. Motta, T. Y. Takeshita, E. Lee, J. A. Latone, and J. M. Garcia,Quantum com- putation of dominant products in lithium–sulfur bat- teries, The Journal of Chemical Physics154, 134115 (2021)

  14. [14]

    Arute, K

    F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, S. Boixo, M. Broughton, B. B. Buckley, D. A. Buell,et al.,Hartree-Fock on a superconducting qubit quantum computer, Science369, 1084 (2020)

  15. [15]

    Q. Gao, G. O. Jones, M. Motta, M. Sugawara, H. C. Watanabe, T. Kobayashi, E. Watanabe, Y.-y. Ohnishi, H. Nakamura, and N. Yamamoto,Applications of quan- tum computing for investigations of electronic transi- tions in phenylsulfonyl-carbazole TADF emitters, npj Computational Materials7, 70 (2021)

  16. [16]

    Kawashima, E

    Y. Kawashima, E. Lloyd, M. P. Coons, Y. Nam, S. Mat- suura, A. J. Garza, S. Johri, L. Huntington, V. Seni- court, A. O. Maksymov, J. H. V. Nguyen, J. Kim, N. Alidoust, A. Zaribafiyan, and T. Yamazaki,Opti- mizing electronic structure simulations on a trapped-ion quantum computer using problem decomposition, Com- munications Physics4, 245 (2021)

  17. [17]

    Eddins, M

    A. Eddins, M. Motta, T. P. Gujarati, S. Bravyi, A. Mez- zacapo, C. Hadfield, and S. Sheldon,Doubling the size of quantum simulators by entanglement forging, PRX Quantum3, 010309 (2022)

  18. [18]

    Yamamoto, D

    K. Yamamoto, D. Z. Manrique, I. T. Khan, H. Sawada, and D. M. Ramo,Quantum hardware calculations of periodic systems with partition-measurement symme- try verification: Simplified models of hydrogen chain and iron crystals, Physical Review Research4, 033110 (2022)

  19. [19]

    J. J. M. Kirsopp, C. Di Paola, D. Z. Manrique, M. Krompiec, G. Greene-Diniz, W. Guba, A. Mey- der, D. Wolf, M. Strahm, and D. Mu˜ noz Ramo,Quan- tum computational quantification of protein–ligand in- teractions, International Journal of Quantum Chemistry 122, 1 (2022)

  20. [20]

    Huang, X

    K. Huang, X. Cai, H. Li, Z.-Y. Ge, R. Hou, H. Li, T. Liu, Y. Shi, C. Chen, D. Zheng,et al.,Variational quantum computation of molecular linear response properties on a superconducting quantum processor, The Journal of Physical Chemistry Letters13, 9114 (2022)

  21. [21]

    Lolur, M

    P. Lolur, M. Skogh, W. Dobrautz, C. Warren, J. Bizn´ arov´ a, A. Osman, G. Tancredi, G. Wendin, J. By- lander, and M. Rahm,Reference-state error mitigation: A strategy for high accuracy quantum computation of chemistry, Journal of Chemical Theory and Computa- tion19, 783 (2023)

  22. [22]

    Leyton-Ortega, S

    V. Leyton-Ortega, S. Majumder, and R. C. Pooser, Quantum error mitigation by hidden inverses protocol in superconducting quantum devices, Quantum Science and Technology8, 014008 (2022)

  23. [23]

    Liang, J

    Z. Liang, J. Cheng, H. Ren, H. Wang, F. Hua, Z. Song, Y. Ding, F. Chong, S. Han, Y. Shi, and X. Qian,NAPA: Intermediate-level variational native-pulse ansatz for variational quantum algorithms, arXiv preprint (2023), 2208.01215

  24. [24]

    Motta, G

    M. Motta, G. O. Jones, J. E. Rice, T. P. Gujarati, R. Sakuma, I. Liepuoniute, J. M. Garcia, and Y.-y. Ohnishi,Quantum chemistry simulation of ground-and excited-state properties of the sulfonium cation on a su- perconducting quantum processor, Chemical Science14, 2915 (2023)

  25. [25]

    T. E. O’Brien, G. Anselmetti, F. Gkritsis, V. Elfving, S. Polla, W. J. Huggins, O. Oumarou, K. Kechedzhi, D. Abanin, R. Acharya,et al.,Purification-based quan- tum error mitigation of pair-correlated electron simula- tions, Nature Physics19, 1787 (2023). 12

  26. [26]

    I. Khan, M. Tudorovskaya, J. Kirsopp, D. Mu˜ noz Ramo, P. Warrier, D. Papanastasiou, and R. Singh,Chemically aware unitary coupled cluster with ab initio calculations on an ion trap quantum computer: A refrigerant chemi- cals’ application, The Journal of Chemical Physics158, 10.1063/5.0144680 (2023)

  27. [27]

    L. Zhao, J. Goings, K. Shin, W. Kyoung, J. I. Fuks, J.-K. Kevin Rhee, Y. M. Rhee, K. Wright, J. Nguyen, J. Kim,et al.,Orbital-optimized pair-correlated elec- tron simulations on trapped-ion quantum computers, npj Quantum Information9, 60 (2023)

  28. [28]

    S. Guo, J. Sun, H. Qian, M. Gong, Y. Zhang, F. Chen, Y. Ye, Y. Wu, S. Cao, K. Liu,et al.,Experimental quantum computational chemistry with optimized uni- tary coupled cluster ansatz, Nature Physics20, 1240 (2024)

  29. [29]

    Weaving, A

    T. Weaving, A. Ralli, W. M. Kirby, P. J. Love, S. Succi, and P. V. Coveney,Benchmarking noisy intermediate scale quantum error mitigation strategies for ground state preparation of the HCl molecule, Phys. Rev. Res. 5, 043054 (2023)

  30. [30]

    P. Liu, R. Wang, J.-N. Zhang, Y. Zhang, X. Cai, H. Xu, Z. Li, J. Han, X. Li, G. Xue,et al.,Performing SU(d) operations and rudimentary algorithms in a supercon- ducting transmon qudit for d= 3 and d= 4, Physical Review X13, 021028 (2023)

  31. [31]

    Dimitrov, G

    E. Dimitrov, G. Sanchez-Sanz, J. Nelson, L. O’Riordan, M. Doyle, S. Courtney, V. Kannan, H. Naseri, A. G. Garcia, J. Tricker,et al.,Pushing the limits of quan- tum computing for simulating PFAS chemistry, arXiv preprint (2023), 2311.01242

  32. [32]

    M. A. Jones, H. J. Vallury, and L. C. Hollenberg, Ground-state-energy calculation for the water molecule on a superconducting quantum processor, Physical Re- view Applied21, 064017 (2024)

  33. [33]

    Liang, Z

    Z. Liang, Z. Song, J. Cheng, H. Ren, T. Hao, R. Yang, Y. Shi, and T. Li, inProceedings of the 61st ACM/IEEE Design Automation Conference(2024) pp. 1–6

  34. [34]

    Weaving, A

    T. Weaving, A. Ralli, P. J. Love, S. Succi, and P. V. Coveney,Contextual subspace variational quan- tum eigensolver calculation of the dissociation curve of molecular nitrogen on a superconducting quantum com- puter, npj Quantum Information11, 25 (2025)

  35. [35]

    J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Bab- bush, and H. Neven,Barren plateaus in quantum neural network training landscapes, Nature communications9, 1 (2018)

  36. [36]

    Holmes, K

    Z. Holmes, K. Sharma, M. Cerezo, and P. J. Coles,Con- necting ansatz expressibility to gradient magnitudes and barren plateaus, PRX Quantum3, 010313 (2022)

  37. [37]

    Cerezo, M

    M. Cerezo, M. Larocca, D. Garc´ ıa-Mart´ ın, N. L. Diaz, P. Braccia, E. Fontana, M. S. Rudolph, P. Bermejo, A. Ijaz, S. Thanasilp,et al.,Does provable absence of barren plateaus imply classical simulability?, Nature Communications16, 7907 (2025)

  38. [38]

    Leone, S

    L. Leone, S. F. Oliviero, L. Cincio, and M. Cerezo,On the practical usefulness of the hardware efficient ansatz, Quantum8, 1395 (2024)

  39. [39]

    Arute, K

    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, Nature574, 505 (2019)

  40. [40]

    Zhong, H

    H.-S. Zhong, H. Wang, Y.-H. Deng, M.-C. Chen, L.- C. Peng, Y.-H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu, et al.,Quantum computational advantage using photons, Science370, 1460 (2020)

  41. [41]

    Wu, W.-S

    Y. Wu, W.-S. Bao, S. Cao, F. Chen, M.-C. Chen, X. Chen, T.-H. Chung, H. Deng, Y. Du, D. Fan,et al., Strong quantum computational advantage using a super- conducting quantum processor, Physical review letters 127, 180501 (2021)

  42. [42]

    L. S. Madsen, F. Laudenbach, M. F. Askarani, F. Rortais, T. Vincent, J. F. Bulmer, F. M. Miatto, L. Neuhaus, L. G. Helt, M. J. Collins,et al.,Quantum computational advantage with a programmable photonic processor, Nature606, 75 (2022)

  43. [43]

    Kanno, M

    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 comput- ers, arXiv preprint (2023), 2302.11320

  44. [44]

    Y. O. Nakagawa, M. Kamoshita, W. Mizukami, S. Sudo, and Y.-y. Ohnishi,ADAPT-QSCI: Adaptive construc- tion of an input state for quantum-selected configuration interaction, Journal of Chemical Theory and Computa- tion20, 10817 (2024)

  45. [45]

    Robledo-Moreno, M

    J. Robledo-Moreno, M. Motta, H. Haas, A. Javadi- Abhari, P. Jurcevic, W. Kirby, S. Martiel, K. Sharma, S. Sharma, T. Shirakawa,et al.,Chemistry beyond the scale of exact diagonalization on a quantum-centric su- percomputer, Science Advances11, eadu9991 (2025)

  46. [46]

    Alexeev, M

    Y. Alexeev, M. Amsler, M. A. Barroca, S. Bassini, T. Battelle, D. Camps, D. Casanova, Y. J. Choi, F. T. Chong, C. Chung,et al.,Quantum-centric supercomput- ing for materials science: A perspective on challenges and future directions, Future Generation Computer Sys- tems160, 666 (2024)

  47. [47]

    Wintersperger, H

    K. Wintersperger, H. Safi, and W. Mauerer, inInter- national Conference on Architecture of Computing Sys- tems(Springer, 2022) pp. 100–114

  48. [48]

    T. Beck, A. Baroni, R. Bennink, G. Buchs, E. A. C. P´ erez, M. Eisenbach, R. F. da Silva, M. G. Meena, K. Gottiparthi, P. Groszkowski,et al.,Integrating quan- tum computing resources into scientific HPC ecosys- tems, Future Generation Computer Systems161, 11 (2024)

  49. [49]

    T. M. Bickley, A. Mingare, T. Weaving, M. Williams de la Bastida, S. Wan, M. Nibbi, P. Seitz, A. Ralli, P. J. Love, M. Chung, M. Hern´ andez Vera, L. Schulz, and P. V. Coveney,Extending quantum computing through subspace, embedding and classical molecular dynamics techniques, Digital Discovery (2025)

  50. [50]

    Matsuzawa and Y

    Y. Matsuzawa and Y. Kurashige,Jastrow-type decom- position in quantum chemistry for low-depth quantum circuits, Journal of chemical theory and computation 16, 944 (2020)

  51. [51]

    Motta, K

    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 struc- ture, Chemical Science14, 11213 (2023)

  52. [52]

    Kaliakin, A

    D. Kaliakin, A. Shajan, J. R. Moreno, Z. Li, A. Mi- tra, M. Motta, C. Johnson, A. A. Saki, S. Das, I. Sit- dikov,et al.,Accurate quantum-centric simulations of supramolecular interactions, arXiv preprint (2024), 2410.09209

  53. [53]

    Barison, J

    S. Barison, J. R. Moreno, and M. Motta,Quantum- centric computation of molecular excited states with ex- 13 tended sample-based quantum diagonalization, Quantum Science and Technology10, 025034 (2025)

  54. [54]

    Duriez, P

    A. Duriez, P. C. Carvalho, M. A. Barroca, F. Zipoli, B. Jaderberg, R. N. B. Ferreira, K. Sharma, A. Mezza- capo, B. Wunsch, and M. Steiner,Computing band gaps of periodic materials via sample-based quantum diago- nalization, arXiv preprint (2025), 2503.10901

  55. [55]

    M. A. Barroca, T. Gujarati, V. Sharma, R. N. B. Fer- reira, Y.-H. Na, M. Giammona, A. Mezzacapo, B. Wun- sch, and M. Steiner,Surface reaction simulations for battery materials through sample-based quantum diago- nalization and local embedding, arXiv preprint (2025), 2503.10923

  56. [56]

    Nogaki, S

    K. Nogaki, S. Backes, T. Shirakawa, S. Yunoki, and R. Arita,Symmetry-adapted sample-based quan- tum diagonalization: Application to lattice model, arXiv preprint (2025), 2505.00914

  57. [57]

    Liepuoniute, K

    I. Liepuoniute, K. D. Doney, J. Robledo-Moreno, J. A. Job, W. S. Friend, and G. O. Jones,Quantum-centric study of methylene singlet and triplet states, arXiv preprint (2024), 2411.04827

  58. [58]

    Kaliakin, A

    D. Kaliakin, A. Shajan, F. Liang, and K. M. Merz Jr, Implicit solvent sample-based quantum diagonalization, The Journal of Physical Chemistry B129, 5788 (2025)

  59. [59]

    Bazayeva, Z

    M. Bazayeva, Z. Li, D. Kaliakin, F. Liang, A. Sha- jan, S. Das, and K. M. Merz Jr,Quantum-centric al- chemical free energy calculations, arXiv preprint (2025), 2506.20825

  60. [60]

    Sriluckshmy, F

    P. Sriluckshmy, F. Jamet, and F.ˇSimkovic IV,Quantum assisted ghost gutzwiller ansatz, arXiv preprint (2025), 2506.21431

  61. [61]

    Shajan, D

    A. Shajan, D. Kaliakin, A. Mitra, J. Robledo Moreno, Z. Li, M. Motta, C. Johnson, A. A. Saki, S. Das, I. Sit- dikov,et al.,Toward quantum-centric simulations of ex- tended molecules: Sample-based quantum diagonaliza- tion enhanced with density matrix embedding theory, Journal of Chemical Theory and Computation21, 6801 (2025)

  62. [62]

    Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging

    T. Smith, T. P. Gujarati, M. Motta, B. Link, I. Liepuo- niute, T. Friedhoff, H. Nishimura, N. Nguyen, K. S. Williams, J. R. Moreno,et al.,Quantum-centric sim- ulation of hydrogen abstraction by sample-based quan- tum diagonalization and entanglement forging, arXiv preprint (2025), 2508.08229

  63. [63]

    Danilov, J

    D. Danilov, J. Robledo-Moreno, K. J. Sung, M. Motta, and J. Shee,Enhancing the accuracy and efficiency of sample-based quantum diagonalization with phase- less auxiliary-field quantum monte carlo, arXiv preprint (2025), 2503.05967

  64. [64]

    Hafid, H

    A. Hafid, H. Iwakiri, K. Tsubouchi, N. Yoshioka, and M. Kohda,Hardness of classically sampling quantum chemistry circuits, arXiv preprint (2025), 2504.12893

  65. [65]

    M. S. Rudolph and J. Tindall,Simulating and sampling from quantum circuits with 2D tensor networks, arXiv preprint (2025), 2507.11424

  66. [66]

    Yoffe, N

    D. Yoffe, N. Entin, A. Natan, and A. Makmal,A qubit- efficient variational selected configuration-interaction method, Quantum Science and Technology10, 015020 (2024)

  67. [67]

    N¨ utzel, A

    L. N¨ utzel, A. Gresch, L. Hehn, L. Marti, R. Freund, A. Steiner, C. D. Marciniak, T. Eckstein, N. Stockinger, S. Wolf,et al.,Solving an industrially relevant quantum chemistry problem on quantum hardware, Quantum Sci- ence and Technology10, 015066 (2025)

  68. [68]

    Bauer, K

    N. Bauer, K. Yeter-Aydeniz, and G. Siopsis,Efficient quantum chemistry calculations on noisy quantum hard- ware, arXiv preprint (2025), 2503.02778

  69. [69]

    Ohgoe, H

    T. Ohgoe, H. Iwakiri, K. Ichikawa, S. Koh, and M. Ko- hda,Quantum computation of a quasiparticle band structure with the quantum-selected configuration inter- action, arXiv preprint (2025), 2504.00309

  70. [70]

    Shirai, S.-Y

    S. Shirai, S.-Y. Tseng, H. Iwakiri, T. Horiba, H. Hirai, and S. Koh,Enhancing accuracy of quantum-selected configuration interaction calculations using multiref- erence perturbation theory: Application to aromatic molecules, arXiv preprint (2025), 2503.22221

  71. [71]

    A. A. Holmes, N. M. Tubman, and C. Umrigar,Heat- bath configuration interaction: An efficient selected con- figuration interaction algorithm inspired by heat-bath sampling, Journal of chemical theory and computation 12, 3674 (2016)

  72. [72]

    Sharma, A

    S. Sharma, A. A. Holmes, G. Jeanmairet, A. Alavi, and C. J. Umrigar,Semistochastic heat-bath configura- tion interaction method: Selected configuration interac- tion with semistochastic perturbation theory, Journal of chemical theory and computation13, 1595 (2017)

  73. [73]

    J. Li, M. Otten, A. A. Holmes, S. Sharma, and C. J. Umrigar,Fast semistochastic heat-bath configura- tion interaction, The Journal of chemical physics149, 10.1063/1.5055390 (2018)

  74. [74]

    Sugisaki, S

    K. Sugisaki, S. Kanno, T. Itoko, R. Sakuma, and N. Yamamoto,Hamiltonian simulation-based quantum- selected configuration interaction for large-scale elec- tronic structure calculations with a quantum computer, arXiv preprint (2024), 2412.07218

  75. [75]

    Mikkelsen and Y

    M. Mikkelsen and Y. O. Nakagawa,Quantum-selected configuration interaction with time-evolved state, arXiv preprint (2024), 2412.13839

  76. [76]

    Oumarou, P

    O. Oumarou, P. J. Ollitrault, C. L. Cortes, M. Scheurer, R. M. Parrish, and C. Gogolin,Molecular properties from quantum Krylov subspace diagonalization, Journal of Chemical Theory and Computation21, 4543 (2025)

  77. [77]

    J. Yu, J. R. Moreno, J. Iosue, L. Bertels, D. Claudino, B. Fuller, P. Groszkowski, T. S. Humble, P. Jurce- vic, W. Kirby, T. A. Maier, M. Motta, B. Pokharel, A. Seif, A. Shehata, K. J. Sung, M. C. Tran, V. Tri- pathi, A. Mezzacapo, and K. Sharma,Sample-based Krylov quantum diagonalization, arXiv preprint (2025), arXiv:2501.09702

  78. [78]

    Piccinelli, A

    S. Piccinelli, A. Baiardi, M. Rossmannek, A. C. Vazquez, F. Tacchino, S. Mensa, E. Altamura, A. Alavi, M. Motta, J. Robledo-Moreno,et al.,Quantum chem- istry with provable convergence via randomized sample- based quantum diagonalization, arXiv preprint (2025), 2508.02578

  79. [79]

    Reinholdt, K

    P. Reinholdt, K. M. Ziems, E. R. Kjellgren, S. Co- riani, S. P. Sauer, and J. Kongsted,Critical limi- tations in quantum-selected configuration interaction methods, Journal of Chemical Theory and Computation 10.1021/acs.jctc.5c00375 (2025)

  80. [80]

    Campbell,Random compiler for fast hamiltonian simulation, Physical review letters123, 070503 (2019)

    E. Campbell,Random compiler for fast hamiltonian simulation, Physical review letters123, 070503 (2019)

Showing first 80 references.