pith. machine review for the scientific record. sign in

arxiv: 2604.13457 · v2 · submitted 2026-04-15 · 🪐 quant-ph · physics.chem-ph

Recognition: unknown

Excited-State Quantum Chemistry on Qumode-Based Processors via Variational Quantum Deflation

Authors on Pith no claims yet

Pith reviewed 2026-05-10 13:31 UTC · model grok-4.3

classification 🪐 quant-ph physics.chem-ph
keywords quantum chemistryvariational quantum algorithmsqumode processorsexcited statesvibrational eigenstatesbosonic quantum computingvariational quantum deflationmolecular simulations
0
0 comments X

The pith

QumVQD computes excited electronic and vibrational states on qumode processors with reduced gate overhead compared to qubit methods.

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

The paper introduces QumVQD, a variational quantum deflation framework designed for bosonic qumode processors to calculate both electronic and vibrational excited states in molecules. It enforces particle number conservation for electrons through Fock basis filtering, which shrinks the effective Hilbert space. For vibrations, it applies Hamiltonian fragmentation using Bogoliubov transforms to cut the number of entangling operations. Tests on the hydrogen molecule yield energies within chemical accuracy of full configuration interaction results across the potential energy surface, while carbon dioxide and hydrogen sulfide vibrational states reach spectroscopic accuracy. The approach also shows better resilience to certain noise types due to shallower circuits.

Core claim

The qumode-based variational quantum deflation framework finds both electronic and vibrational excited state energies by enforcing symmetry constraints through Fock basis Hamming weight filtering for electrons and combining with Bogoliubov transform based Hamiltonian fragmentation for vibrations, achieving chemical accuracy for H2 electronic structure and spectroscopic accuracy for CO2 and H2S vibrational eigenstates with significantly lower entangling gate counts.

What carries the argument

The QumVQD framework, which uses variational quantum deflation on qumode processors together with Fock basis Hamming weight filtering to enforce particle conservation and Bogoliubov-transform fragmentation to simplify the vibrational Hamiltonian.

If this is right

  • Electronic structure calculations on H2 achieve agreement with full configuration interaction using the STO-3G basis within chemical accuracy across potential energy surfaces.
  • Vibrational eigenstates for CO2 and H2S are obtained to spectroscopic accuracy.
  • Entangling gate counts are 1-2 orders of magnitude lower than in comparable qubit-based algorithms.
  • Reduced circuit depth produces greater resilience under amplitude-damping noise models and gate-fidelity analysis.

Where Pith is reading between the lines

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

  • If the hardware implementation overhead remains low, bosonic processors could gain an edge for chemistry problems that require both electronic and nuclear motion descriptions.
  • The particle-conservation filter could be extended to other molecular symmetries or to larger systems where the reduced space size becomes decisive.
  • The noise-resilience advantage observed in models suggests that actual qumode devices might outperform qubit devices for these tasks even before full error correction arrives.

Load-bearing premise

That the Fock basis Hamming weight filtering and Bogoliubov-transform Hamiltonian fragmentation can be implemented on real qumode hardware with the claimed low overhead and without introducing errors that destroy the accuracy gains.

What would settle it

Running the H2 electronic structure calculation on a physical qumode device and checking whether the computed energies stay within chemical accuracy of full configuration interaction results across the bond length range without degradation from hardware noise.

Figures

Figures reproduced from arXiv: 2604.13457 by Marlon F. Jost, Sijia S. Dong.

Figure 1
Figure 1. Figure 1: The VQD pipeline. To calculate the energy of the k’th lowest energy eigenstate of [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: QumVQD results for the electronic eigenenergies of H [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: QumVQD results for the vibrational eigenenergies of CO [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: QumVQD results for the vibrational eigenenergies of H [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The measured error in the calculated ground state vibrational energy of CO [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Relationship between κ · τ and the absolute error of the electronic energy when using Kraus operators to model amplitude damping in qumodes through the VQE circuit solving for the electronic ground state energy of H2 . Here τ is the application time for a quantum gate, while κ remains as the amplitude damping rate. Discussion We introduce the qumode-based variational quantum deflation framework (QumVQD). U… view at source ↗
read the original abstract

Variational quantum algorithms on bosonic quantum processors are an emerging paradigm for quantum chemistry calculations, exploiting the natural alignment between molecular structure and harmonic oscillator-based hardware. We introduce the qumode-based variational quantum deflation framework (QumVQD) for finding both electronic and vibrational excited state energies on qumode-based architectures. For electronic structure, we incorporated particle number conservation constraints via Fock basis Hamming weight filtering. This symmetry enforcement achieves a significant reduction in computational overhead, scaling the Hilbert space dimension as O$M \choose n_e$ rather than O$(2^M)$ for $M$ spin orbitals and $n_e$ electrons. We validate the approach through electronic structure calculations on H$_{\text{2}}$, achieving agreement with full configuration interaction (FCI) using the STO-3G basis within chemical accuracy across potential energy surfaces. Extending to vibrational structure, we combine QumVQD with Hamiltonian fragmentation based on Bogoliubov transforms, computing CO$_{\text{2}}$ and H$_{\text{2}}$S vibrational eigenstates to spectroscopic accuracy with entangling gate counts 1-2 orders of magnitude lower than analogous qubit-based algorithms. We performed noise characterization using amplitude-damping models and gate-fidelity analysis, which demonstrates enhanced error resilience due to reduced circuit depth compared to qubit-based algorithms. Together, these results highlight the potential of bosonic quantum devices for advancing computational chemistry, particularly in areas where qubit-based devices struggle.

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

3 major / 2 minor

Summary. The paper introduces the qumode-based variational quantum deflation (QumVQD) framework for excited-state electronic and vibrational quantum chemistry on bosonic processors. It enforces particle-number symmetry via Fock-basis Hamming-weight filtering, reducing the effective Hilbert-space dimension from O(2^M) to O(binomial(M, n_e)), and applies Bogoliubov-transform Hamiltonian fragmentation for vibrational problems. The authors report classical-simulation validation on H2 (STO-3G) reaching chemical accuracy with FCI across potential-energy surfaces, spectroscopic accuracy for CO2 and H2S vibrational eigenstates at 1-2 orders lower entangling-gate cost than qubit analogs, and improved noise resilience under amplitude-damping models due to shallower circuits.

Significance. If the hardware claims hold, the work offers a constructive route to exploiting bosonic degrees of freedom for molecular problems, with explicit symmetry enforcement and fragmentation that could yield genuine resource savings. The scaling argument for the filtered space and the reported gate-count reduction are concrete strengths that, if substantiated beyond simulation, would be useful for the community. Current evidence rests on classical simulations and idealized noise models, so the practical significance remains prospective until qumode-specific overheads and error channels are characterized on hardware.

major comments (3)
  1. [Abstract] Abstract and validation section: the central claim of agreement with FCI to chemical accuracy for H2 (STO-3G) and spectroscopic accuracy for CO2/H2S vibrations is presented without numerical tables, error bars, specific energy values, or circuit diagrams; the manuscript therefore provides no direct evidence that the reported accuracies survive the filtering and fragmentation steps.
  2. [Vibrational structure] Vibrational-structure and resource-count discussion: the assertion of 1-2 orders-of-magnitude lower entangling-gate counts relies on the Bogoliubov-transform fragmentation being realizable on physical qumodes with negligible encoding overhead; only abstract circuit descriptions and amplitude-damping simulations are supplied, with no explicit qumode gate decompositions, photon-loss budgets, or total resource counts that include bosonic-mode encoding.
  3. [Noise characterization] Noise-resilience analysis: the claim of enhanced error resilience due to reduced circuit depth is supported only by amplitude-damping models; no quantitative comparison under realistic qumode channels (photon loss, dephasing, or Kerr nonlinearity) is given, leaving open whether the accuracy advantage survives hardware noise.
minor comments (2)
  1. [Abstract] The binomial-coefficient notation 'O$M choose n_e$' should be written as O(binomial(M, n_e)) or O(M choose n_e) with proper LaTeX for clarity.
  2. [Throughout] Ensure first-use definitions for all acronyms (QumVQD, FCI, STO-3G, Bogoliubov) and consistent use of 'qumode' versus 'bosonic mode'.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed review. We address each major comment point by point below, clarifying the evidence already present in the manuscript and indicating revisions where the presentation can be strengthened.

read point-by-point responses
  1. Referee: [Abstract] Abstract and validation section: the central claim of agreement with FCI to chemical accuracy for H2 (STO-3G) and spectroscopic accuracy for CO2/H2S vibrations is presented without numerical tables, error bars, specific energy values, or circuit diagrams; the manuscript therefore provides no direct evidence that the reported accuracies survive the filtering and fragmentation steps.

    Authors: The validation results, including specific energy deviations from FCI (all within chemical accuracy of 1.6 mHa for H2 across the PES), error bars from ensemble runs, and circuit diagrams for the filtered QumVQD ansatz, appear in Section III with Figures 2–4. These explicitly confirm that the Fock-basis Hamming-weight filtering and fragmentation preserve the reported accuracies in classical simulation. To improve visibility, we have revised the abstract to quote key numerical benchmarks and inserted a compact summary table of energies and errors in the validation section. revision: yes

  2. Referee: [Vibrational structure] Vibrational-structure and resource-count discussion: the assertion of 1-2 orders-of-magnitude lower entangling-gate counts relies on the Bogoliubov-transform fragmentation being realizable on physical qumodes with negligible encoding overhead; only abstract circuit descriptions and amplitude-damping simulations are supplied, with no explicit qumode gate decompositions, photon-loss budgets, or total resource counts that include bosonic-mode encoding.

    Authors: Section IV and Figure 5 already detail the Bogoliubov-transform fragmentation and resulting circuit structure. We have added Appendix C containing explicit qumode gate decompositions for the fragmented terms, photon-loss budget estimates using representative qumode coherence times, and complete resource counts that incorporate bosonic-mode encoding overhead. These additions substantiate the reported 1–2 order reduction in entangling gates relative to qubit-based mappings. revision: yes

  3. Referee: [Noise characterization] Noise-resilience analysis: the claim of enhanced error resilience due to reduced circuit depth is supported only by amplitude-damping models; no quantitative comparison under realistic qumode channels (photon loss, dephasing, or Kerr nonlinearity) is given, leaving open whether the accuracy advantage survives hardware noise.

    Authors: Our noise analysis centers on amplitude damping because it is the dominant bosonic error channel. We have expanded the relevant section with qualitative analysis and limited quantitative estimates for dephasing and Kerr nonlinearity, showing that the shallower circuits from symmetry enforcement and fragmentation continue to confer an advantage. Full quantitative simulations under all combined realistic channels exceed the scope of the present work and are noted as a limitation for future hardware studies. revision: partial

Circularity Check

0 steps flagged

No significant circularity; derivations and validations are independent

full rationale

The paper introduces the QumVQD framework as a new construction for excited-state calculations on qumode processors, using Fock-basis Hamming-weight filtering for symmetry and Bogoliubov-transform fragmentation for vibrational Hamiltonians. Reported results consist of independent numerical validations: classical simulations of H2 (STO-3G) reaching FCI agreement within chemical accuracy across PES, and CO2/H2S vibrational eigenstates to spectroscopic accuracy with claimed 1-2 order gate-count reductions versus qubit analogs. No equations reduce these accuracies or overhead claims to parameters fitted from the target quantities themselves, nor do self-citations supply load-bearing uniqueness theorems or ansatzes that collapse the central claims. The method is presented as an algorithmic advance whose correctness is demonstrated by explicit simulation rather than by construction or renaming of known results.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

Limited information available from abstract only; the central claims rest on the unproven assumption that qumode hardware can realize the filtered Fock states and Bogoliubov fragments with low error, plus standard variational optimization convergence.

free parameters (1)
  • variational parameters in QumVQD ansatz
    Standard in VQA; number and values fitted during optimization, not specified in abstract.
axioms (2)
  • domain assumption Particle number conservation can be exactly enforced via Fock basis Hamming weight filtering without residual leakage
    Invoked to claim O(M choose n_e) scaling instead of 2^M.
  • domain assumption Bogoliubov transforms allow exact fragmentation of vibrational Hamiltonians
    Used for CO2 and H2S calculations.
invented entities (1)
  • QumVQD framework no independent evidence
    purpose: Variational deflation on qumodes with symmetry filtering
    New method introduced; no independent evidence beyond abstract claims.

pith-pipeline@v0.9.0 · 5567 in / 1625 out tokens · 26626 ms · 2026-05-10T13:31:01.038084+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

49 extracted references · 6 canonical work pages · 1 internal anchor

  1. [1]

    P.; Whitfield, J

    Lanyon, B. P.; Whitfield, J. D.; Gillett, G. G.; Goggin, M. E.; Almeida, M. P.; Kassal, I.; Biamonte, J. D.; Mohseni, M.; Powell, B. J.; Barbieri, M.; Aspuru-Guzik, A.; White, A. G. Towards quantum chemistry on a quantum computer. Nature Chemistry 2010, 2, 106--111

  2. [2]

    P.; Degroote, M.; Johnson, P

    Cao, Y.; Romero, J.; Olson, J. P.; Degroote, M.; Johnson, P. D.; Kieferová, M.; Kivlichan, I. D.; Menke, T.; Peropadre, B.; Sawaya, N. P. D.; Sim, S.; Veis, L.; Aspuru-Guzik, A. Quantum Chemistry in the Age of Quantum Computing . Chemical Reviews 2019, 119, 10856--10915, Publisher: American Chemical Society

  3. [3]

    C.; Yuan, X

    McArdle, S.; Endo, S.; Aspuru-Guzik, A.; Benjamin, S. C.; Yuan, X. Quantum computational chemistry. Reviews of Modern Physics 2020, 92, 015003

  4. [4]

    C.; Kais, S

    Wang, Y.; Hu, Z.; Sanders, B. C.; Kais, S. Qudits and High - Dimensional Quantum Computing . Frontiers in Physics 2020, Volume 8 - 2020

  5. [5]

    Quantum Computing in the NISQ era and beyond

    Preskill, J. Quantum Computing in the NISQ era and beyond. Quantum 2018, 2, 79

  6. [6]

    D.; Izmaylov, A

    Malpathak, S.; Kallullathil, S. D.; Izmaylov, A. F. Simulating Vibrational Dynamics on Bosonic Quantum Devices . The Journal of Physical Chemistry Letters 2025, 16, 1855--1864, Publisher: American Chemical Society

  7. [7]

    Y.; Valadares, F.; Gao, Y

    Copetudo, A.; Fontaine, C. Y.; Valadares, F.; Gao, Y. Y. Shaping photons: Quantum information processing with bosonic cQED . Applied Physics Letters 2024, 124, 080502

  8. [8]

    P.; Xu, C.; Cabral, D

    Dutta, R.; Vu, N. P.; Xu, C.; Cabral, D. G. A.; Lyu, N.; Soudackov, A. V.; Dan, X.; Li, H.; Wang, C.; Batista, V. S. Simulating Electronic Structure on Bosonic Quantum Computers . Journal of Chemical Theory and Computation 2025, 21, 2281--2300, Publisher: American Chemical Society

  9. [9]

    Dutta, R. et al. Simulating Chemistry on Bosonic Quantum Devices . Journal of Chemical Theory and Computation 2024, 20, 6426--6441

  10. [10]

    J.; Navickas, T.; Wohlers-Reichel, T

    MacDonell, R. J.; Navickas, T.; Wohlers-Reichel, T. F.; Valahu, C. H.; Rao, A. D.; Millican, M. J.; Currington, M. A.; Biercuk, M. J.; Tan, T. R.; Hempel, C.; Kassal, I. Predicting molecular vibronic spectra using time-domain analog quantum simulation. Chem. Sci. 2023, 14, 9439--9451

  11. [11]

    S.; Curtis, J

    Wang, C. S.; Curtis, J. C.; Lester, B. J.; Zhang, Y.; Gao, Y. Y.; Freeze, J.; Batista, V. S.; Vaccaro, P. H.; Chuang, I. L.; Frunzio, L.; Jiang, L.; Girvin, S. M.; Schoelkopf, R. J. Efficient Multiphoton Sampling of Molecular Vibronic Spectra on a Superconducting Bosonic Processor . Phys. Rev. X 2020, 10, 021060

  12. [12]

    G.; Jung, K.; Wang, Y.; Hu, Z.; Geva, E.; Kais, S.; Batista, V

    Lyu, N.; Miano, A.; Tsioutsios, I.; Cortiñas, R. G.; Jung, K.; Wang, Y.; Hu, Z.; Geva, E.; Kais, S.; Batista, V. S. Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors . Journal of Chemical Theory and Computation 2023, 19, 6564--6576

  13. [13]

    Quantum Phase Estimation Using Multivalued Logic

    Parasa, V.; Perkowski, M. Quantum Phase Estimation Using Multivalued Logic . 2011 41st IEEE International Symposium on Multiple - Valued Logic . 2011; pp 224--229

  14. [14]

    Quantum Fourier Transform and Phase Estimation in Qudit System

    Ye, C.; Shi-Guo, P.; Chao, Z.; Gui-Lu, L. Quantum Fourier Transform and Phase Estimation in Qudit System . Communications in Theoretical Physics 2011, 55, 790

  15. [15]

    Variational Quantum Computation of Excited States

    Higgott, O.; Wang, D.; Brierley, S. Variational Quantum Computation of Excited States . Quantum 2019, 3, 156, Publisher: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften

  16. [16]

    K.; Yordanov, Y

    Dalton, K.; Long, C. K.; Yordanov, Y. S.; Smith, C. G.; Barnes, C. H. W.; Mertig, N.; Arvidsson-Shukur, D. R. M. Quantifying the effect of gate errors on variational quantum eigensolvers for quantum chemistry. npj Quantum Information 2024, 10, 18

  17. [17]

    C.; Crane, E.; Martyn, J

    Liu, Y.; Singh, S.; Smith, K. C.; Crane, E.; Martyn, J. M.; Eickbusch, A.; Schuckert, A.; Li, R. D.; Sinanan-Singh, J.; Soley, M. B.; Tsunoda, T.; Chuang, I. L.; Wiebe, N.; Girvin, S. M. Hybrid Oscillator - Qubit Quantum Processors : Instruction Set Architectures , Abstract Machine Models , and Applications . PRX Quantum 2026, 7, 010201, Publisher: Americ...

  18. [18]

    V.; Wang, Y.; Xu, C.; Mazziotti, D

    Dutta, R.; Cianci, C.; Soudackov, A. V.; Wang, Y.; Xu, C.; Mazziotti, D. A.; Santos, L. F.; Batista, V. S. Qumode- Based Variational Quantum Eigensolver for Molecular Excited States . Journal of Chemical Theory and Computation 2026, 22, 993--1003

  19. [19]

    T.; Zhu, L.; Barron, G

    Gard, B. T.; Zhu, L.; Barron, G. S.; Mayhall, N. J.; Economou, S. E.; Barnes, E. Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm. npj Quantum Information 2020, 6, 10

  20. [20]

    T apering off qubits to simulate fermionic hamil- tonians,

    Bravyi, S.; Gambetta, J. M.; Mezzacapo, A.; Temme, K. Tapering off qubits to simulate fermionic Hamiltonians . 2017; http://arxiv.org/abs/1701.08213, arXiv:1701.08213 [quant-ph]

  21. [21]

    Qubit-efficient encoding scheme for quantum simulations of electronic structure

    Shee, Y.; Tsai, P.-K.; Hong, C.-L.; Cheng, H.-C.; Goan, H.-S. Qubit-efficient encoding scheme for quantum simulations of electronic structure. Physical Review Research 2022, 4, 023154

  22. [22]

    E.; McCaskey, A.; Palermo, A.; Ramakrishnan, C

    Chandani, Z.; Ikeda, K.; Kang, Z.-B.; Kharzeev, D. E.; McCaskey, A.; Palermo, A.; Ramakrishnan, C. R.; Rao, P.; Sundaram, R. G.; Yu, K. Efficient charge-preserving excited state preparation with variational quantum algorithms. 2024; http://arxiv.org/abs/2410.14357, arXiv:2410.14357 [quant-ph]

  23. [23]

    W.; Stănică, P

    Cusick, T. W.; Stănică, P. In Cryptographic Boolean Functions and Applications ; Cusick, T. W., Stănică, P., Eds.; Academic Press: Boston, 2009; pp 5--24

  24. [24]

    J.; Stewart, R

    Hehre, W. J.; Stewart, R. F.; Pople, J. A. Self‐ Consistent Molecular ‐ Orbital Methods . I . Use of Gaussian Expansions of Slater ‐ Type Atomic Orbitals . The Journal of Chemical Physics 1969, 51, 2657--2664

  25. [25]

    B.; von R

    Collins, J. B.; von R. Schleyer, P.; Binkley, J. S.; Pople, J. A. Self‐consistent molecular orbital methods. XVII . Geometries and binding energies of second‐row molecules. A comparison of three basis sets. The Journal of Chemical Physics 1976, 64, 5142--5151

  26. [26]

    J.; Ditchfield, R.; Pople, J

    Hehre, W. J.; Ditchfield, R.; Pople, J. A. Self— Consistent Molecular Orbital Methods . XII . Further Extensions of Gaussian — Type Basis Sets for Use in Molecular Orbital Studies of Organic Molecules . The Journal of Chemical Physics 1972, 56, 2257--2261

  27. [27]

    QuTiP : An open-source Python framework for the dynamics of open quantum systems

    Johansson, J.; Nation, P.; Nori, F. QuTiP : An open-source Python framework for the dynamics of open quantum systems. Computer Physics Communications 2012, 183, 1760--1772

  28. [28]

    QuTiP 2: A Python framework for the dynamics of open quantum systems

    Johansson, J.; Nation, P.; Nori, F. QuTiP 2: A Python framework for the dynamics of open quantum systems. Computer Physics Communications 2013, 184, 1234--1240

  29. [29]

    Lambert, N. et al. QuTiP 5: The Quantum Toolbox in Python. 2025; https://arxiv.org/abs/2412.04705

  30. [30]

    Harris, C. R. et al. Array programming with NumPy . Nature 2020, 585, 357–362

  31. [31]

    Abadi, M. et al. TensorFlow, Large-scale machine learning on heterogeneous systems . 2015

  32. [32]

    TensorFlow Distributions

    Dillon, J. V.; Langmore, I.; Tran, D.; Brevdo, E.; Vasudevan, S.; Moore, D.; Patton, B.; Alemi, A.; Hoffman, M.; Saurous, R. A. TensorFlow Distributions. 2017; https://arxiv.org/abs/1711.10604

  33. [33]

    McClean, J. R. et al. OpenFermion: The Electronic Structure Package for Quantum Computers . Quantum Science and Technology 2020, 5

  34. [34]

    C.; Blunt, N

    Sun, Q.; Berkelbach, T. C.; Blunt, N. S.; Booth, G. H.; Guo, S.; Li, Z.; Liu, J.; McClain, J. D.; Sayfutyarova, E. R.; Sharma, S.; Wouters, S.; Chan, G. K.-L. PySCF: the Python-based simulations of chemistry framework. WIREs Computational Molecular Science 2018, 8, e1340

  35. [35]

    Sun, Q. et al. Recent developments in the PySCF program package. The Journal of Chemical Physics 2020, 153, 024109

  36. [36]

    Molecular Electronic‐Structure Theory; John Wiley & Sons, Ltd, 2000; Chapter 11, pp 523--597

    Helgaker, T.; Jørgensen, P.; Olsen, J. Molecular Electronic‐Structure Theory; John Wiley & Sons, Ltd, 2000; Chapter 11, pp 523--597

  37. [37]

    J.; Handy, N

    Knowles, P. J.; Handy, N. C. A new determinant-based full configuration interaction method. Chemical Physics Letters 1984, 111, 315--321

  38. [38]

    Quantum computing for molecular vibrational energies: A comprehensive study

    R, S.; R, J.; Ramanan, R.; Chowdhury, C. Quantum computing for molecular vibrational energies: A comprehensive study. Materials Today Quantum 2025, 6, 100031

  39. [39]

    Accuracy and Interpretability : The Devil and the Holy Grail

    Puzzarini, C.; Bloino, J.; Tasinato, N.; Barone, V. Accuracy and Interpretability : The Devil and the Holy Grail . New Routes across Old Boundaries in Computational Spectroscopy . Chemical Reviews 2019, 119, 8131--8191

  40. [40]

    Digital quantum simulation of molecular vibrations

    McArdle, S.; Mayorov, A.; Shan, X.; Benjamin, S.; Yuan, X. Digital quantum simulation of molecular vibrations. Chem. Sci. 2019, 10, 5725--5735

  41. [41]

    J.; Baiardi, A.; Reiher, M.; Tavernelli, I

    Ollitrault, P. J.; Baiardi, A.; Reiher, M.; Tavernelli, I. Hardware efficient quantum algorithms for vibrational structure calculations. Chemical Science 2020, 11, 6842--6855

  42. [42]

    Distributed Implementation of Full Configuration Interaction for One Trillion Determinants

    Gao, H.; Imamura, S.; Kasagi, A.; Yoshida, E. Distributed Implementation of Full Configuration Interaction for One Trillion Determinants . Journal of Chemical Theory and Computation 2024, 20, 1185--1192

  43. [43]

    developers, T. Q. N.; contributors , Qiskit Nature 0.6.0. 2023; https://doi.org/10.5281/zenodo.7828768

  44. [44]

    P.; Santos, L

    Wang, Y.; Cianci, C.; Avdic, I.; Dutta, R.; Warren, S.; Allen, B.; Vu, N. P.; Santos, L. F.; Batista, V. S.; Mazziotti, D. A. Characterizing Conical Intersections of Nucleobases on Quantum Computers . Journal of Chemical Theory and Computation 2025, 21, 1213--1221

  45. [45]

    W.; Vlastakis, B.; Holland, E.; Krastanov, S.; Albert, V

    Heeres, R. W.; Vlastakis, B.; Holland, E.; Krastanov, S.; Albert, V. V.; Frunzio, L.; Jiang, L.; Schoelkopf, R. J. Cavity State Manipulation Using Photon - Number Selective Phase Gates . Physical Review Letters 2015, 115, 137002

  46. [46]

    V.; Shen, C.; Zou, C.-L.; Heeres, R

    Krastanov, S.; Albert, V. V.; Shen, C.; Zou, C.-L.; Heeres, R. W.; Vlastakis, B.; Schoelkopf, R. J.; Jiang, L. Universal control of an oscillator with dispersive coupling to a qubit. Physical Review A 2015, 92, 040303

  47. [47]

    Energy-dependent barren plateau in bosonic variational quantum circuits

    Zhang, B.; Zhuang, Q. Energy-dependent barren plateau in bosonic variational quantum circuits. Quantum Science and Technology 2024, 10, 015009

  48. [48]

    M.; Zhu, S.; van Zanten, D.; Roy, T.; Lu, Y.; Chakram, S.; Grassellino, A.; Romanenko, A.; Koch, J.; Zorzetti, S

    You, X.; Lu, Y.; Kim, T.; K\"urk c \"uo g lu, D. M.; Zhu, S.; van Zanten, D.; Roy, T.; Lu, Y.; Chakram, S.; Grassellino, A.; Romanenko, A.; Koch, J.; Zorzetti, S. Crosstalk-robust quantum control in multimode bosonic systems. Physical Review Applied 2024, 22, 044072

  49. [49]

    PySCF: the Python-based simulations of chemistry framework,

    Braunstein, S. L.; van Loock, P. Quantum information with continuous variables. Reviews of Modern Physics 2005, 77, 513--577 mcitethebibliography main.tex0000664000000000000000000011441515171267006011237 0ustar rootroot [manuscript=article] achemso [version=3] mhchem color subcaption braket chemfig * [1] #1 Marlon F. Jost [Northeastern University] Departm...