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arxiv: 2606.30551 · v1 · pith:LIUPK7XEnew · submitted 2026-06-29 · 🪐 quant-ph · cs.LG· physics.chem-ph

Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

Anurag K. S. V. , Ashish Kumar Patra , Manas Mukherjee , Ruchika Bhat , Sai Shankar P. , Rahul Maitra , Jaiganesh G This is my paper

Pith reviewed 2026-06-30 05:58 UTC · model grok-4.3

classification 🪐 quant-ph cs.LGphysics.chem-ph
keywords quantum selected CIUCCSD ansatzrestricted Boltzmann machinedensity matrix embedding theoryprotein-ligand bindingNISQ quantum computingmolecular electronic structurehybrid quantum-classical methods
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The pith

Integrating a linear-scaling ansatz and a generative machine learning model into quantum-selected configuration interaction cuts the classical resources needed for molecular binding energy calculations.

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

The paper develops a workflow that combines the linear scaling CNOT UCCSD ansatz with quantum selected configuration interaction to lower the cost of parameter initialization from O(N^6) to O(N^4). It further introduces a restricted Boltzmann machine to handle configuration recovery in the subspace expansion. These changes are tested on small molecules and applied to embedded calculations for Amantadine and a SARS-CoV-2 protease complex. The result is claimed to require only a fraction of the classical resources used in previous state-of-the-art simulations for similar systems.

Core claim

The authors claim that integrating LCNot-UCCSD into the QSCI framework and employing QSCI-RBM for generative modeling allows accurate electronic structure calculations on NISQ-era simulators for protein-ligand systems, achieving this with substantially reduced classical computational overhead compared to existing methods.

What carries the argument

The LCNot-UCCSD ansatz providing O(N^4) MP2-based initialization for the QSCI procedure, together with the RBM acting as a compact generative model for subspace expansion in place of traditional recovery methods.

Load-bearing premise

The assumption that performance on an ideal state-vector simulator with artificial error levels will carry over to real NISQ hardware without significant additional costs from error mitigation or increased circuit depths.

What would settle it

Running the QSCI-RBM workflow on physical quantum processors for one of the tested molecules and measuring whether the resource savings and accuracy are maintained under real noise conditions.

Figures

Figures reproduced from arXiv: 2606.30551 by Anurag K. S. V., Ashish Kumar Patra, Jaiganesh G, Manas Mukherjee, Rahul Maitra, Ruchika Bhat, Sai Shankar P..

Figure 1
Figure 1. Figure 1: Amantadine fragmentation scheme. The 28-atom molecule is partitioned into 11 fragments (F1–F11) for the DMET calculation. Reproduced from our work on IQM Ref. [15]. Mpro–Carmofur. PDB 7BUY [19]; active region extracted from the crystal structure (X-ray, 1.60 Å resolution); 10 fragments; partition [2, 5, 5, 5, 6, 5, 6, 6, 6, 7] (geometry: Appendix 5.2). Without fragmentation or active-space selection, the a… view at source ↗
Figure 2
Figure 2. Figure 2: Mpro-Carmofur complex and fragmentation scheme. (a) Full protein-ligand complex visualized from the PDB 7BUY crystal structure. (b) The selected active region extracted for the quantum simulation. (c) Detailed fragmentation scheme of the active region, illustrating the partitioning boundaries across the protein and ligand segments for the DMET calculation. 3 Results 3.1 Quantum Circuit Resources [PITH_FUL… view at source ↗
Figure 3
Figure 3. Figure 3: SQD(LCNot-UCCSD) and QSCI(LCNot-UCCSD)-RBM: energy deviation from FCI and diagonalization subspace coverage across all artificial-error levels for eight molecules in STO-3G. (a) SQD energy error ∆E = |ESQD − EFCI| vs. σ (scatter = individual runs; line = median; shaded band = inter-quartile range, IQR, over 100 independent runs). The dashed horizontal line marks chemical accuracy (1.59 mHa = 1 kcal mol−1 )… view at source ↗
Figure 4
Figure 4. Figure 4: Median |E − EFCI| (mHa) heatmaps across the 14-point σ sweep for eight molecules in STO-3G. Green borders indicate cells within chemical accuracy (≤ 1.59 mHa). (a) SQD(LCNot-UCCSD): chemical accuracy is achieved only from σ ≥ 0.2 (LiH, BeH2) to σ ≥ 2.0 (CO). At lower noise levels, SQD gives median errors of 12-271 mHa, two to five orders of magnitude outside chemical accuracy. (b) QSCI(LCNot-UCCSD)-RBM: ev… view at source ↗
Figure 5
Figure 5. Figure 5: N2 (4HOMO-4LUMO) cc-pVDZ Potential Energy Surface Scan. Top Row: Absolute PES curves comparing HF, MP2, CCSD, CASCI, and the mean zero-error (σ = 0) recovery by SQD (left) and QSCI-RBM (right). Middle Row: Absolute mean energy deviation |∆E| from the CASCI reference across multiple σ levels. Chemical accuracy (1.59 mHa) is indicated by the dashed black line. Bottom Row: mean diagonalization subspace covera… view at source ↗
Figure 6
Figure 6. Figure 6: Median |∆ECASCI| Heatmaps for N2 (4HOMO-4LUMO) cc-pVDZ. Energy deviations across 20 geometries and 14 artificial error levels (σ). Green bounding boxes indicate cells that successfully fall within chemical accuracy (1.59 mHa). QSCI-RBM consistently achieves chemical accuracy for all σ, whereas SQD requires heuristic regularization at high σ values to converge [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Diagonalization Subspace Coverage Heatmaps. Mean coverage ratio ηsub corresponding to the energy evaluations in [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Dynamic Subspace Scaling against N2 Multireference Character. Direct comparison of the mean diagonalization subspace coverage (ηsub = |Ssub|/|Ssym|) across the stretching of the bond length. QSCI(LCNot-UCCSD)-RBM at zero artificial error (σ = 0) adaptively scales the required subspace dimension with multireference character, from ∼31% at the compressed geometry, through ∼50-70% near equilibrium, to ∼100% a… view at source ↗
Figure 9
Figure 9. Figure 9: DMET-SQD(LCNot-UCCSD) and DMET-QSCI(LCNot-UCCSD)-RBM results for Amantadine across three basis sets. Each row corresponds to one basis set (STO-3G, 6-31G, cc-pVDZ, top to bottom). Left column: |∆E| from DMET-CASCI (mHa, log scale) vs. artificial-error amplitude σ. Dashed horizontal line: chemical accuracy (1.59 mHa). Right column: diagonalization subspace coverage ηsub = |Ssub|/|Ssym| (mean ± std across 11… view at source ↗
Figure 10
Figure 10. Figure 10: DMET-SQD(LCNot-UCCSD) and DMET-QSCI(LCNot-UCCSD)-RBM results for the Mpro–Carmofur active region (PDB 7BUY) across three basis sets. Layout and legend identical to [PITH_FULL_IMAGE:figures/full_fig_p024_10.png] view at source ↗
read the original abstract

Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths. This report presents a hybrid quantum-classical workflow performed on the Fujitsu FX700 ideal state-vector simulator using QARP that addresses two structural inefficiencies in quantum-sampling-based diagonalization workflows. First, we integrate the Linear Scaling CNOT UCCSD (LCNot-UCCSD) ansatz into the QSCI framework, replacing the $\mathcal{O}(N^6)$ CCSD parameter initialization of the competing LUCJ ansatz approach with $\mathcal{O}(N^4)$ MP2-amplitude initialization. Second, we introduce QSCI-RBM, a variant that replaces the configuration recovery of the SQD framework with a Restricted Boltzmann Machine (RBM) acting as a compact generative subspace expansion model. Both are evaluated on eight different molecules in STO-3G across 14 controlled artificial error levels with 100 independent runs each, validated on potential energy surface scans of the N$_2$ molecule in cc-pVDZ, and embedded within DMET to treat the FDA-approved antiviral Amantadine (C$_{10}$H$_{17}$N, 11 DMET fragments) and the active region of the SARS-CoV-2 main protease complexed with its covalent inhibitor Carmofur (PDB: 7BUY, C$_{15}$H$_{28}$N$_4$O$_5$S, 10 fragments). To our knowledge, this is the first deployment of LCNot-UCCSD within QSCI on a quantum computing simulator, and the first DMET-QSCI(LCNot-UCCSD)-RBM application to an industry-relevant protein-ligand system. By utilizing a fraction of the classical computing resources required by the current state-of-the-art work by Cleveland Clinic, RIKEN, and IBM Quantum, this approach enables more efficient and economical drug discovery simulations for the industry.

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 / 0 minor

Summary. The paper presents a hybrid quantum-classical workflow for molecular electronic structure calculations that integrates the LCNot-UCCSD ansatz into the QSCI framework (replacing O(N^6) CCSD initialization with O(N^4) MP2 amplitudes) and introduces QSCI-RBM, which uses a Restricted Boltzmann Machine for configuration recovery in place of SQD methods. The approach is evaluated on an ideal state-vector simulator for eight molecules in STO-3G across 14 artificial error levels (100 runs each), N2 PES scans in cc-pVDZ, and DMET embeddings of two protein-ligand systems (Amantadine and the SARS-CoV-2 main protease with Carmofur), with the central claim being that it uses a fraction of the classical resources required by prior Cleveland Clinic/RIKEN/IBM work.

Significance. If the unshown numerical results and error metrics support the efficiency claims, the combination of a cheaper ansatz initialization and generative-model subspace expansion could reduce the classical overhead in quantum-selected CI workflows and extend their reach to larger embedded systems relevant to drug discovery. The work also supplies the first reported use of LCNot-UCCSD inside QSCI and the first DMET-QSCI-RBM application to an industry protein-ligand complex.

major comments (2)
  1. [Abstract] Abstract: The evaluation protocol (8 molecules, 14 error levels, 100 runs, PES scans, DMET on two systems) is described in detail, yet the manuscript supplies no numerical results, error bars, timing data, or comparison tables. Without these data the headline claim that the method uses only a fraction of the classical resources of the Cleveland Clinic/RIKEN/IBM reference cannot be assessed.
  2. [Abstract] Abstract and evaluation description: All reported runs are performed on the Fujitsu FX700 ideal state-vector simulator with controlled artificial error levels. No data or analysis is given on circuit-depth overhead, real-device noise, or additional error-mitigation costs that would appear on actual NISQ hardware; if these costs exceed the simulated savings, both the resource-comparison claim and the drug-discovery applicability statement fail.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and for highlighting these important points regarding the presentation of results and the scope of the simulations. We address each comment below and commit to revisions that strengthen the manuscript without overstating its current content.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The evaluation protocol (8 molecules, 14 error levels, 100 runs, PES scans, DMET on two systems) is described in detail, yet the manuscript supplies no numerical results, error bars, timing data, or comparison tables. Without these data the headline claim that the method uses only a fraction of the classical resources of the Cleveland Clinic/RIKEN/IBM reference cannot be assessed.

    Authors: We agree that the current manuscript version does not present the numerical results, error bars, timing data, or comparison tables needed to substantiate the resource-efficiency claim. This omission prevents independent assessment of the headline statement. In the revised manuscript we will add the full set of simulation outcomes (including per-molecule error metrics with standard deviations from the 100 runs, wall-clock timings, and direct resource comparisons) in the Results section and will insert a concise quantitative summary into the abstract. revision: yes

  2. Referee: [Abstract] Abstract and evaluation description: All reported runs are performed on the Fujitsu FX700 ideal state-vector simulator with controlled artificial error levels. No data or analysis is given on circuit-depth overhead, real-device noise, or additional error-mitigation costs that would appear on actual NISQ hardware; if these costs exceed the simulated savings, both the resource-comparison claim and the drug-discovery applicability statement fail.

    Authors: The evaluations were intentionally restricted to an ideal state-vector simulator to isolate the algorithmic contributions of LCNot-UCCSD initialization and RBM-based recovery. We will add a dedicated subsection that reports the circuit depths required by the LCNot-UCCSD ansatz, estimates the two-qubit gate counts, and discusses how standard error-mitigation techniques (e.g., zero-noise extrapolation or probabilistic error cancellation) could be combined with the workflow. We will also qualify the drug-discovery applicability statement to reflect that real-hardware overheads remain to be quantified experimentally. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivations introduce independent components without reduction to inputs or self-citations.

full rationale

The paper defines LCNot-UCCSD via MP2 amplitudes (O(N^4)) replacing CCSD initialization and introduces QSCI-RBM as a generative model for subspace expansion, both presented as novel integrations into the QSCI framework. These steps are evaluated via simulator runs but do not reduce by construction to fitted parameters renamed as predictions, self-definitions, or load-bearing self-citations. The resource-efficiency claim rests on explicit comparisons to external prior work rather than internal tautologies, leaving the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the workflow inherits standard UCCSD/MP2 and RBM assumptions from quantum chemistry and machine learning without new postulates.

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discussion (0)

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Reference graph

Works this paper leans on

46 extracted references · 38 canonical work pages · 4 internal anchors

  1. [1]

    Ligand-binding affinity estimates supported by quantum- mechanical methods,

    U. Ryde and P. Söderhjelm, “Ligand-binding affinity estimates supported by quantum- mechanical methods,”Chemical Reviews, vol. 116, no. 9, pp. 5520–5566, Apr 2016. [Online]. Available: https://doi.org/10.1021/acs.chemrev.5b00630

    work page doi:10.1021/acs.chemrev.5b00630 2016

  2. [2]

    Sqm2.20: Semiempirical quantum- mechanical scoring function yields dft-quality protein–ligand binding affinity predictions in minutes,

    A. Pecina, J. Fanfrlík, M. Lepšík, and J. Řezáč, “Sqm2.20: Semiempirical quantum- mechanical scoring function yields dft-quality protein–ligand binding affinity predictions in minutes,”Nature Communications, vol. 15, no. 1, p. 1127, Feb 2024. [Online]. Available: https://doi.org/10.1038/s41467-024-45431-8

    work page doi:10.1038/s41467-024-45431-8 2024

  3. [3]

    Distributed implementation of full configuration interaction for one trillion determinants,

    H. Gao, S. Imamura, A. Kasagi, and E. Yoshida, “Distributed implementation of full configuration interaction for one trillion determinants,”Journal of Chemical Theory and Computation, vol. 20, no. 3, pp. 1185–1192, Feb 2024. [Online]. Available: https://doi.org/10.1021/acs.jctc.3c01190

    work page doi:10.1021/acs.jctc.3c01190 2024

  4. [4]

    Numerically exact configuration interaction at quadrillion-determinant scale,

    A. Shayit, C. Liao, S. Upadhyay, H. Hu, T. Zhang, A. E. DePrince III, C. Yang, and X. Li, “Numerically exact configuration interaction at quadrillion-determinant scale,”Nature Communications, vol. 16, no. 1, p. 11016, Dec 2025. [Online]. Available: https://doi.org/10.1038/s41467-025-65967-7

    work page doi:10.1038/s41467-025-65967-7 2025

  5. [5]

    Szabo and N

    A. Szabo and N. S. Ostlund,Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, 1st ed., ser. Dover Books on Chemistry. Mineola, NY: Dover Publications, 1996. [Online]. Available: https://books.google.co.in/books?id= k-DcCgAAQBAJ

    1996

  6. [6]

    Helgaker, P

    T. Helgaker, P. Jørgensen, and J. Olsen,Molecular Electronic-Structure Theory, 1st ed. John Wiley & Sons, Ltd, 2000. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/ 10.1002/9781119019572.ch1

    work page doi:10.1002/9781119019572.ch1 2000

  7. [7]

    Insights into current limitations of density functional theory,

    A. J. Cohen, P. Mori-Sánchez, and W. Yang, “Insights into current limitations of density functional theory,”Science, vol. 321, no. 5890, pp. 792–794, Aug 2008. [Online]. Available: https://www.science.org/doi/abs/10.1126/science.1158722

    work page doi:10.1126/science.1158722 2008

  8. [8]

    Challenges for density functional theory,

    ——, “Challenges for density functional theory,”Chemical Reviews, vol. 112, no. 1, pp. 289–320, Dec 2012. [Online]. Available: https://doi.org/10.1021/cr200107z

    work page doi:10.1021/cr200107z 2012

  9. [9]

    Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers

    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,” Feb 2023. [Online]. Available: https://doi.org/10.48550/arXiv.2302.11320

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2302.11320 2023

  10. [10]

    Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer,

    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,”Science Advances, vol. 11, no. 25, p. eadu9991, Jun

  11. [11]

    Available: https://www.science.org/doi/abs/10.1126/sciadv.adu9991

    [Online]. Available: https://www.science.org/doi/abs/10.1126/sciadv.adu9991

    work page doi:10.1126/sciadv.adu9991

  12. [12]

    Successive approximations by the rayleigh-ritz variation method,

    J. K. L. MacDonald, “Successive approximations by the rayleigh-ritz variation method,”Phys. Rev., vol. 43, pp. 830–833, May 1933. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRev.43.830

    work page doi:10.1103/physrev.43.830 1933

  13. [13]

    Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes

    J. Kenneth M. Merz, A. Shajan, D. Kaliakin, F. Liang, Y. Otsuka, T. Shirakawa, L. Broers, H. Xu, M. Tsuji, M. Sato, S. Yunoki, R. Wakizaka, Y. Kawashima, J. Doi, T. Itoko, H. Horii, T. Pellegrini, J. R. Moreno, K. J. Sung, E. Fejer, R. Walkup, S. Seelam, and M. Motta, “Crossing the 12,000-atom barrier with heterogeneous quantum-classical REFERENCES 29 sup...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.01138 2026

  14. [14]

    Linear-scaling quantum circuits for computational chemistry,

    I. Magoulas and F. A. Evangelista, “Linear-scaling quantum circuits for computational chemistry,”Journal of Chemical Theory and Computation, vol. 19, no. 15, pp. 4815–4821, Aug 2023. [Online]. Available: https://doi.org/10.1021/acs.jctc.3c00376

    work page doi:10.1021/acs.jctc.3c00376 2023

  15. [15]

    Machine-learnedcompactsubspacegenerationforquantumselectedconfigurationinteraction within density matrix embedding framework,

    A. K. Patra, K. S. V. Anurag, R. Maitra, R. Bhat, P. Sai Shankar, and G. Jaiganesh, “Machine-learnedcompactsubspacegenerationforquantumselectedconfigurationinteraction within density matrix embedding framework,” 2026, manuscript in preparation

    2026

  16. [16]

    Towards chemically accurate and scalable quantum simulations on iqm quantum hardware: A quantum-hpc hybrid approach,

    K. S. V. Anurag, A. K. Patra, M. Mukherjee, A. Shukla, P. Sai Shankar, R. Bhat, T. S. L. Radhika, and G. Jaiganesh, “Towards chemically accurate and scalable quantum simulations on iqm quantum hardware: A quantum-hpc hybrid approach,” Apr 2026. [Online]. Available: https://doi.org/10.48550/arXiv.2604.01983

    work page doi:10.48550/arxiv.2604.01983 2026

  17. [17]

    Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster jastrow ansatz for electronic structure,

    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., vol. 14, pp. 11213–11227, Sep 2023. [Online]. Available: http://dx.doi.org/10.1039/D3SC02516K

    work page doi:10.1039/d3sc02516k 2023

  18. [18]

    Density matrix embedding: A simple alternative to dynamical mean-field theory,

    G. Knizia and G. K.-L. Chan, “Density matrix embedding: A simple alternative to dynamical mean-field theory,”Phys. Rev. Lett., vol. 109, p. 186404, Nov 2012. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.109.186404

    work page doi:10.1103/physrevlett.109.186404 2012

  19. [19]

    Structure of mpro from sars-cov-2 and discovery of its inhibitors,

    Z. Jin, X. Du, Y. Xu, Y. Deng, M. Liu, Y. Zhao, B. Zhang, X. Li, L. Zhang, C. Peng, Y. Duan, J. Yu, L. Wang, K. Yang, F. Liu, R. Jiang, X. Yang, T. You, X. Liu, X. Yang, F. Bai, H. Liu, X. Liu, L. W. Guddat, W. Xu, G. Xiao, C. Qin, Z. Shi, H. Jiang, Z. Rao, and H. Yang, “Structure of mpro from sars-cov-2 and discovery of its inhibitors,”Nature, vol. 582, ...

    work page doi:10.1038/s41586-020-2223-y 2020

  20. [20]

    Structural basis for the inhibition of sars-cov-2 main protease by antineoplastic drug carmofur,

    Z. Jin, Y. Zhao, Y. Sun, B. Zhang, H. Wang, Y. Wu, Y. Zhu, C. Zhu, T. Hu, X. Du, Y. Duan, J. Yu, X. Yang, X. Yang, K. Yang, X. Liu, L. W. Guddat, G. Xiao, L. Zhang, H. Yang, and Z. Rao, “Structural basis for the inhibition of sars-cov-2 main protease by antineoplastic drug carmofur,”Nature Structural & Molecular Biology, vol. 27, no. 6, pp. 529–532, Jun. ...

    work page doi:10.1038/s41594-020-0440-6 2020

  21. [21]

    Antiviral amantadine,

    T. Nisar, H. Sutherland-Foggio, and W. Husar, “Antiviral amantadine,”The Lancet Neurology, vol. 18, no. 12, p. 1080, Dec 2019. [Online]. Available: https://doi.org/10.1016/S1474-4422(19)30361-8

    work page doi:10.1016/s1474-4422(19)30361-8 2019

  22. [22]

    Cnot-efficient circuits for arbitrary rank many-body fermionic and qubit excitations,

    I. Magoulas and F. A. Evangelista, “Cnot-efficient circuits for arbitrary rank many-body fermionic and qubit excitations,”Journal of Chemical Theory and Computation, vol. 19, no. 3, pp. 822–836, 2023. [Online]. Available: https://doi.org/10.1021/acs.jctc.2c01016

    work page doi:10.1021/acs.jctc.2c01016 2023

  23. [23]

    Über das paulische äquivalenzverbot,

    P. Jordan and E. Wigner, “Über das paulische äquivalenzverbot,”Zeitschrift für Physik, vol. 47, no. 9, pp. 631–651, 1928. [Online]. Available: https://doi.org/10.1007/BF01331938

    work page doi:10.1007/bf01331938 1928

  24. [24]

    Resource estimation for vqe on small molecules: Impact of fermion mappings and hamiltonian reductions,

    K. S. V. Anurag, A. K. Patra, V. D. Ghevade, P. Sai Shankar, R. Bhat, V. Raghavendra, R. Maitra, and G. Jaiganesh, “Resource estimation for vqe on small molecules: Impact of fermion mappings and hamiltonian reductions,”Journal of Computational Chemistry, vol. 47, no. 11, p. e70379, Apr 2026, e70379 3014834. [Online]. Available: https://onlinelibrary.wiley...

    work page doi:10.1002/jcc.70379 2026

  25. [25]

    Quantum algorithms for electronic structure calculations: Particle-hole hamiltonian and optimized wave-function expansions,

    P. K. Barkoutsos, J. F. Gonthier, I. Sokolov, N. Moll, G. Salis, A. Fuhrer, M. Ganzhorn, D. J. Egger, M. Troyer, A. Mezzacapo, S. Filipp, and I. Tavernelli, “Quantum algorithms for electronic structure calculations: Particle-hole hamiltonian and optimized wave-function expansions,”Phys. Rev. A, vol. 98, p. 022322, Aug 2018. [Online]. Available: https://li...

    work page doi:10.1103/physreva.98.022322 2018

  26. [26]

    Parallel implementation of high-fidelity multiqubit gates with neutral atoms,

    H. Levine, A. Keesling, G. Semeghini, A. Omran, T. T. Wang, S. Ebadi, H. Bernien, M. Greiner, V. Vuletić, H. Pichler, and M. D. Lukin, “Parallel implementation of high-fidelity multiqubit gates with neutral atoms,”Phys. Rev. Lett., vol. 123, p. 170503, Oct

  27. [27]

    Available: https://link.aps.org/doi/10.1103/PhysRevLett.123.170503

    [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.123.170503

    work page doi:10.1103/physrevlett.123.170503

  28. [28]

    High-fidelity parallel entangling gates on a neutral-atom quantum computer,

    S. J. Evered, D. Bluvstein, M. Kalinowski, S. Ebadi, T. Manovitz, H. Zhou, S. H. Li, A. A. Geim, T. T. Wang, N. Maskara, H. Levine, G. Semeghini, M. Greiner, V. Vuletić, and M. D. Lukin, “High-fidelity parallel entangling gates on a neutral-atom quantum computer,”Nature, vol. 622, no. 7982, pp. 268–272, Oct 2023. [Online]. Available: https://doi.org/10.10...

    work page doi:10.1038/s41586-023-06481-y 2023

  29. [29]

    Quantum 4:327, ISSN 2521-327X, ://dx.doi.org/10.22331/q-2020-09-21-327

    L. Henriet, L. Beguin, A. Signoles, T. Lahaye, A. Browaeys, G.-O. Reymond, and C. Jurczak, “Quantum computing with neutral atoms,”Quantum, vol. 4, p. 327, sep 2020. [Online]. Available: https://doi.org/10.22331/q-2020-09-21-327

    work page doi:10.22331/q-2020-09-21-327 2020

  30. [30]

    Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

    P. Mathiot, E. Garnaoui, A.-U. Leriche, E. Philip, B. Albrecht, C. Briosne-Fréjaville, L. Cardarelli, A. Cornillot, G. Cournez, L. Couturier, J. D. Hond, R. E. Koussaifi, T. Eritzpokoff, F. Fasola, A. A. Gentile, C. Gyurik, C. Hamot, L. Henriet, G. Hercé, M. Kaicher, L. Lassablière, F.-M. L. Régent, E. Leroux, Y. Machu, H. Mamann, L. Ortiz, A. Paine, T. P...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2605.21276 2026

  31. [31]

    Parametrized multiqubit gates for neutral-atom quantum platforms,

    M. Mohan, J. de Hond, and S. Kokkelmans, “Parametrized multiqubit gates for neutral-atom quantum platforms,”Phys. Rev. Appl., vol. 23, p. 054074, May 2025. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevApplied.23.054074

    work page doi:10.1103/physrevapplied.23.054074 2025

  32. [32]

    Quantum Simulation of Ligand-like Molecules through Sample-based Quantum Diagonalization in Density Matrix Embedding Framework

    A. K. Patra, K. S. V. Anurag, P. Sai Shankar, R. Bhat, V. Raghavendra, R. Maitra, and G. Jaiganesh, “Quantum simulation of ligand-like molecules through sample-based quantum diagonalization in density matrix embedding framework,” Apr 2026. [Online]. Available: https://doi.org/10.48550/arXiv.2511.22158

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2511.22158 2026

  33. [33]

    Unsupervised learning of distributions on binary vectors using two layer networks,

    Y. Freund and D. Haussler, “Unsupervised learning of distributions on binary vectors using two layer networks,” inAdvances in Neural Information Processing Systems, J. Moody, S. Hanson, and R. Lippmann, Eds., vol. 4. Morgan-Kaufmann,

  34. [34]

    Available: https://proceedings.neurips.cc/paper_files/paper/1991/file/ 33e8075e9970de0cfea955afd4644bb2-Paper.pdf

    [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/1991/file/ 33e8075e9970de0cfea955afd4644bb2-Paper.pdf

    1991

  35. [35]

    Restricted boltzmann machine learning for solving strongly correlated quantum systems,

    Y. Nomura, A. S. Darmawan, Y. Yamaji, and M. Imada, “Restricted boltzmann machine learning for solving strongly correlated quantum systems,”Phys. Rev. B, vol. 96, p. 205152, Nov 2017. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevB.96.205152

    work page doi:10.1103/physrevb.96.205152 2017

  36. [36]

    Restricted boltzmann machines in quantum physics,

    R. G. Melko, G. Carleo, J. Carrasquilla, and J. I. Cirac, “Restricted boltzmann machines in quantum physics,”Nature Physics, vol. 15, no. 9, pp. 887–892, 2019. [Online]. Available: https://doi.org/10.1038/s41567-019-0545-1

    work page doi:10.1038/s41567-019-0545-1 2019

  37. [37]

    Solving the schrödinger equation in the configuration space with generative machine learning,

    B. Herzog, B. Casier, S. Lebègue, and D. Rocca, “Solving the schrödinger equation in the configuration space with generative machine learning,”Journal of Chemical REFERENCES 31 Theory and Computation, vol. 19, no. 9, pp. 2484–2490, 2023. [Online]. Available: https://doi.org/10.1021/acs.jctc.2c01216

    work page doi:10.1021/acs.jctc.2c01216 2023

  38. [38]

    Physics- informed generative machine learning for accelerated quantum-centric supercomputing,

    C. Patra, D. Mondal, S. Halder, D. Halder, M. R. Laskar, R. Goel, and R. Maitra, “Physics- informed generative machine learning for accelerated quantum-centric supercomputing,”

  39. [39]

    Available: https://doi.org/10.48550/arXiv.2512.06858

    [Online]. Available: https://doi.org/10.48550/arXiv.2512.06858

    work page doi:10.48550/arxiv.2512.06858

  40. [40]

    A practical guide to density matrix embedding theory in quantum chemistry,

    S. Wouters, C. A. Jiménez-Hoyos, Q. Sun, and G. K.-L. Chan, “A practical guide to density matrix embedding theory in quantum chemistry,”Journal of Chemical Theory and Computation, vol. 12, no. 6, pp. 2706–2719, Jun 2016. [Online]. Available: https://doi.org/10.1021/acs.jctc.6b00316

    work page doi:10.1021/acs.jctc.6b00316 2016

  41. [41]

    Toward quantum-centric simulations of extended molecules: Sample-based quantum diagonalization enhanced with density matrix embedding theory,

    A. Shajan, D. Kaliakin, A. Mitra, J. Robledo Moreno, Z. Li, M. Motta, C. Johnson, A. A. Saki, S. Das, I. Sitdikov, A. Mezzacapo, and K. M. Merz, “Toward quantum-centric simulations of extended molecules: Sample-based quantum diagonalization enhanced with density matrix embedding theory,”Journal of Chemical Theory and Computation, vol. 21, no. 14, pp. 6801...

    work page doi:10.1021/acs.jctc.5c00114 2025

  42. [42]

    State-of-the-art density matrix renormalization group and coupled cluster theory studies of the nitrogen binding curve,

    G. K.-L. Chan, M. Kállay, and J. Gauss, “State-of-the-art density matrix renormalization group and coupled cluster theory studies of the nitrogen binding curve,”The Journal of Chemical Physics, vol. 121, no. 13, pp. 6110–6116, Oct 2004. [Online]. Available: https://doi.org/10.1063/1.1783212

    work page doi:10.1063/1.1783212 2004

  43. [43]

    Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization,

    S. Barison, J. Robledo Moreno, and M. Motta, “Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization,”Quantum Science and Technology, vol. 10, no. 2, p. 025034, Feb 2025. [Online]. Available: https://doi.org/10.1088/2058-9565/adb781

    work page doi:10.1088/2058-9565/adb781 2025

  44. [44]

    Qulacs: a fast and versatile quantum circuit simulator for research purpose,

    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, vol. 5, p. 559, Oct

  45. [45]

    Available: https://doi.org/10.22331/q-2021-10-06-559

    [Online]. Available: https://doi.org/10.22331/q-2021-10-06-559

    work page doi:10.22331/q-2021-10-06-559 2021

  46. [46]

    The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices,

    E. R. Davidson, “The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices,”Journal of Computational Physics, vol. 17, no. 1, pp. 87–94, Jan 1975. [Online]. Available: https://doi.org/10.1016/0021-9991(75)90065-0 REFERENCES 32 Supplementary Information A. Molecular Geometries and Fragmenta...

    work page doi:10.1016/0021-9991(75)90065-0 1975