Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double unitary downfolding

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Downfolded Hamiltonians lift VQE accuracy with no extra iterations.

desk verdict A useful, honest benchmark of DUCC downfolding with ADAPT-VQE, but the abstract's 'no extra quantum load' claim is not supported by the reported iteration counts. read the letter →

arxiv 2504.18683 v1 pith:SWDGGESD submitted 2025-04-25 quant-ph

Harjeet Singh , Luke W. Bertels , Daniel Claudino , Sophia E. Economou , Edwin Barnes , Nicholas P. Bauman , Karol Kowalski , Nicholas J. Mayhall This is my paper · ORCID
classification quant-ph MSC 81P6881V55 PACS 03.67.Ac31.15.V
keywords ADAPT-VQEdoubleunitarycoupledclusterHamiltoniandownfoldingeffectiveactivespacedynamicalcorrelationvariationalquantumeigensolverchemistry
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper combines two techniques: ADAPT-VQE, an algorithm that grows a quantum trial state operator by operator, and double unitary coupled cluster (DUCC) downfolding, which classically transforms a molecular Hamiltonian so that correlation outside a chosen active space is folded into an effective active-space Hamiltonian. The central claim is that DUCC effective Hamiltonians, when used with ADAPT-VQE, converge to the ground state in essentially the same number of iterations as the bare active-space Hamiltonian, while recovering dynamical correlation energy from outside the active space. If correct, this means a quantum processor can reach accuracy comparable to a full-basis calculation using only an active-space number of qubits, with no additional algorithmic iterations. The paper tests this across LiH, H6, and H2O, examining strong correlation, truncations of the commutator expansion, higher-body terms, and the choice of classical amplitudes used to build the effective Hamiltonian.

What carries the argument

The central object is the DUCC effective Hamiltonian, defined by similarity-transforming the bare Hamiltonian with the external cluster operator $\hat{\sigma}_{\mathrm{ext}}$ and projecting into the active space. The transformation is expanded with the Baker-Campbell-Hausdorff series, truncated at the A4 or A7 levels and typically reduced to one- and two-body operators, with the option to retain three- and four-body terms. The external amplitudes that define $\hat{\sigma}_{\mathrm{ext}}$ are taken from classical coupled-cluster calculations (CCSD), sometimes replaced by MP2, CCD, or CCSD-with-T1-set-to-zero amplitudes. The second key piece is ADAPT-VQE, which grows a parameterized trial state by appending exponentiated excitations selected by the largest gradient of the energy with respect to pool operators; the paper uses a generalized singles and doubles pool and the Jordan-Wigner mapping. This machinery tests whether the transformed integrals alter the convergence of ADAPT-VQE while recovering external correlation.

What would settle it

Compute DUCC-ADAPT-VQE energies for symmetric stretched H6 at 2 Å using external amplitudes from a multireference method instead of CCSD; if the energy error stays near the CCSD-based value rather than dropping below it, the claim that the classical amplitude source is the limiting factor would be falsified. A separate test: if any molecule shows ADAPT-VQE needing significantly more iterations for a DUCC Hamiltonian than for the bare Hamiltonian, the no-extra-load claim would be falsified.

Watch

Extended reading notes

Core claim

The central claim is that DUCC effective Hamiltonians provide increased accuracy without increasing the load on the quantum processor. When the effective Hamiltonian is built with the A4 or A7 truncations of the Baker-Campbell-Hausdorff expansion and external amplitudes from CCSD, exact diagonalization of the effective Hamiltonian in the active space recovers most of the dynamical correlation energy outside that space; for LiH the A7 effective Hamiltonian reaches errors near 0.06 mHa across the dissociation curve. When ADAPT-VQE is applied to the effective Hamiltonian, it converges to the ground state of the effective Hamiltonian in nearly the same number of iterations as it does for the bare active-space Hamiltonian. Adding three- and four-body terms to the effective Hamiltonian does not change this convergence picture. The paper also finds that the accuracy of the effective Hamiltonian is sensitive to the type of external amplitudes: CCSD amplitudes give the best results, while MP2, CCD, and CCSD-with-T1-zero amplitudes are less accurate, and in strongly correlated regimes where CCSD breaks down, the DUCC approximations inherit that breakdown.

Load-bearing premise

The downfolded Hamiltonian is only as accurate as the classical amplitudes (CCSD, MP2, or CCD) used to build it, so when those amplitudes are wrong, as in stretched H6, the effective Hamiltonian inherits the error.

Editorial extensions

If this is right

  • For molecules where single-reference coupled-cluster amplitudes are reliable, DUCC-ADAPT-VQE can reach near-full-basis accuracy with an active-space-sized qubit register.
  • The number of ADAPT-VQE iterations needed for a given target accuracy is nearly unchanged when the bare Hamiltonian is replaced by a DUCC effective Hamiltonian, even when three- and four-body terms are present.
  • A two-body operator pool remains effective for effective Hamiltonians that contain higher-body operators, so no new operator-pool design is required to use downfolded integrals.
  • The accuracy of the downfolded Hamiltonian is limited by the quality of the classical amplitudes; where CCSD breaks down, the effective Hamiltonian inherits that breakdown.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural next test is to replace CCSD external amplitudes with multireference or self-consistently optimized amplitudes; if the DUCC energy then stays accurate where CCSD fails, it would confirm that the classical amplitude source is the bottleneck.
  • The near-identical iteration counts across bare and downfolded Hamiltonians suggest that the effective Hamiltonian preserves the same easy directions for ansatz growth, so further convergence gains may come from operator-pool design rather than from better integrals.
  • Because DUCC yields a Hermitian effective Hamiltonian, it should combine cleanly with other resource-reduction tools such as qubit tapering or grouped Pauli measurements; the paper does not test this combination.
  • The finding that higher-body terms matter most when $\hat{T}_1$ amplitudes are large suggests a practical rule: systems with a large $\hat{T}_1$ diagnostic should retain three- and four-body terms in the effective Hamiltonian.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript reports a numerical study that combines double unitary coupled cluster (DUCC) effective Hamiltonians with ADAPT-VQE for quantum chemistry. The authors benchmark two truncations of the DUCC commutator expansion (A4 and A7, plus variants including three- and four-body terms) on LiH, H6, and H2O, using external amplitudes from CCSD, MP2, CCD, and CCSD with T1 set to zero. They compare exact diagonalizations of the effective Hamiltonians against FCI (LiH) or extrapolated ASCI (H6, H2O) reference energies, and they compare ADAPT-VQE iteration counts for bare and downfolded Hamiltonians. The central claim, stated in the abstract, is that DUCC Hamiltonians provide increased accuracy without increasing the load on the quantum processor.

Significance. If the central claim were fully established, the work would be a useful step toward NISQ-era quantum chemistry: active-space qubit counts could recover full-basis accuracy without additional ADAPT iterations. The study's strengths are its systematic exploration of commutator truncations and external-amplitude choices, and its honest reporting of failure cases, including non-variational DUCC energies in canonical orbitals (SI S-I) and the breakdown of CCSD-based amplitudes for stretched H6 (Section IV.A.1). The benchmarks use external FCI and ASCI references, and the numerical tables are internally consistent. However, the quantum-resource part of the central claim is not established by the reported iteration counts, and one of the three molecules (H2O) explicitly violates the abstract's 'similar convergence' statement.

major comments (3)
  1. [IV.C, Fig. 2; abstract] The statement that DUCC Hamiltonians provide increased accuracy 'without increasing the load on the quantum processor' is supported only by ADAPT iteration counts, and iteration count is not a complete measure of quantum load. Each ADAPT iteration requires gradient measurements in Eq. (6) and VQE parameter optimization, and the DUCC Hamiltonians of Eqs. (17)-(18) are generally denser and, in the A7(34) case, contain three- and four-body terms; this changes the Pauli-string count, measurement groupings, shot requirements, and circuit depth. No such resource data are reported. Moreover, Section IV.C states that for H2O the algorithm exceeds the 200-iteration limit, which contradicts the abstract's 'similar convergence' claim. The central claim should be scoped to iteration counts or supported by a full resource comparison.
  2. [IV.A.1, Eq. (19), Table II] The accuracy of the effective Hamiltonian depends on the classical external amplitudes, and the paper's own data show this dependence is load-bearing: for stretched H6, CCSD becomes non-variational and the A4/A7 errors grow substantially, with the A7 error increasing from 2.20 mHa at 1 Å to 8.25 mHa at 2 Å when CCSD amplitudes are used. The paper acknowledges this in Section IV.A.1, but the abstract and conclusions state a general accuracy improvement without a strong-correlation caveat. The general claim should be restricted to regimes where the single-reference external amplitudes are reliable, with a quantitative statement of the limitation.
  3. [III, Table I] The H6 and H2O reference energies are extrapolated ASCI energies rather than exact energies, and the manuscript reports no uncertainty estimate for these references. For H2O in particular, Table I shows A7 errors of 6.16 mHa at 1Re and 15.24 mHa at 2Re, both above the 1.59 mHa chemical-accuracy threshold, so the claimed accuracy improvement is not uniform across the tested systems. The conclusions should state the accuracy ceiling implied by the external-amplitude approximation and the reference uncertainty rather than presenting the method as uniformly more accurate.
minor comments (5)
  1. [II.B, Eqs. (17)-(18)] The text says these expansions are 'truncated to two body operators,' but later sections consider A4(3), A7(3), and A7(34) with three- and four-body terms; please clarify the convention and define the notation before first use.
  2. [IV.C, Fig. 2] For H2O at 2Re, please state explicitly whether the ADAPT-VQE calculation eventually converged after exceeding the 200-iteration limit or was terminated at the limit; this is important for interpreting the convergence plot.
  3. [I] There is a typo in 'Grimsely' in the introduction; it should read 'Grimsley'.
  4. [III] Please provide a data-availability or code-availability statement, since the numerical benchmarks use an in-house ADAPT-VQE implementation and would benefit from reproducibility details.
  5. [Tables I and II] Use 'mHa' consistently instead of 'mH' in the table headings and text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: DUCC energies and ADAPT-VQE convergence are benchmarked against external FCI/ASCI references, with no fitted parameters or by-construction reductions.

full rationale

The derivation chain in this paper is self-contained: the DUCC effective Hamiltonian is explicitly constructed in Eqs. (12)-(18) from the Baker-Campbell-Hausdorff expansion of H_ext = e^{-sigma_ext} H e^{sigma_ext}, with external amplitudes obtained from standard classical methods (CCSD, MP2, CCD; Eqs. (19)-(22)). The ground-state energies of these effective Hamiltonians are compared to externally computed FCI (LiH) or extrapolated ASCI (H6, H2O) references, not to quantities derived from the DUCC construction itself. No parameter is fitted to the reference energies, and no term in the effective Hamiltonian is defined in terms of the ADAPT-VQE convergence data being predicted. The observed ADAPT-VQE convergence is an empirical result reported as iteration counts; the abstract's phrase 'without increasing the load' is under-supported because per-iteration measurement cost and circuit depth are not reported and the H2O case exceeds the 200-iteration limit, but that is an inference-strength and completeness limitation, not a circular reduction. The DUCC formalism and A4/A7 truncation labels are cited from prior work by the same authors, but the paper states the formulas it uses and does not invoke those citations to justify the numerical benchmarks; hence the self-citation is not load-bearing in the sense relevant to circularity.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The method introduces no new physical entities and no fitted empirical constants. Its accuracy depends on several methodological choices: active space size, orbital basis, BCH truncation order, and the source of external amplitudes. The most consequential of these is the classical amplitude approximation, which limits the approach to regimes where single-reference coupled cluster is reliable.

free parameters (2)
  • Active space size per molecule = 8 orbitals for LiH, 6 for H6, 9 for H2O
    Chosen by the authors via natural orbital occupation analysis. The accuracy and variationality of DUCC results depend on this choice, as the SI shows different active spaces change the errors and canonical orbitals even give non-variational energies.
  • ASCI extrapolation parameters for reference energies = 1M, 5M, and 10M determinants, extrapolated to zero PT2
    Used to define the reference energies for H6 and H2O. This fitting procedure sets the quality of the benchmark errors, and no uncertainty is reported for the extrapolation.
assumptions (5)
  • standard math The double unitary ansatz |Psi> = exp(sigma_ext) exp(sigma_int) |phi0> is exact for any state given appropriate anti-Hermitian generators.
    Invoked in Eq. (9). The exactness of unitary coupled cluster parameterization is established in the cited literature (Ref. 115).
  • domain assumption Truncating the BCH expansion at second or third order (A4 and A7 variants) and truncating the resulting operators to low body order gives a good approximation to the exact transformed Hamiltonian.
    This is the core approximation of the paper, defined in Eqs. (17) and (18). The paper itself tests this assumption and shows the truncation errors are small for weakly correlated systems but more significant for strongly correlated H6.
  • ad hoc to paper External amplitudes are well approximated by classical CCSD (or MP2, CCD, CCSD with T1=0) amplitudes.
    This is the central approximation of the DUCC construction, stated in Eqs. (19)-(22). The paper explicitly identifies this as the reason for DUCC failure when CCSD breaks down, so the validity of the entire approach rests on this assumption.
  • domain assumption MP2 natural virtual orbitals provide a suitable orbital basis for the DUCC transformation.
    The computational details specify natural virtual orbitals from MP2. The SI shows that using canonical orbitals instead gives non-variational DUCC energies, so the choice of orbital basis is load-bearing and not guaranteed by any theoretical principle.
  • domain assumption The extrapolated Adaptive Sampling CI (ASCI) energies are reliable reference values for H6 and H2O.
    The reference energies are obtained by extrapolating three ASCI calculations to the zero PT2 limit. No error bars are provided, and the extrapolation is an approximate procedure.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double unitary downfolding." pith.science (2026). https://pith.science/paper/SWDGGESD

@misc{pith2026250418683,
  author       = {Pith},
  title        = {Pith review of: Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double unitary downfolding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SWDGGESD}},
  note         = {Machine review of arXiv:2504.18683}
}
read the original abstract

In this work, we combine the recently developed double unitary coupled cluster (DUCC) theory with the adaptive, problem-tailored variational quantum eigensolver (ADAPT-VQE) to explore accuracy of unitary downfolded Hamiltonians for quantum simulation of chemistry. We benchmark the ability of DUCC effective Hamiltonians to recover dynamical correlation energy outside of an active space. We consider the effects of strong correlation, commutator truncation, higher-body terms, and approximate external amplitudes on the accuracy of these effective Hamiltonians. When combining these DUCC Hamiltonians with ADAPT-VQE, we observe similar convergence of the ground state as compared to bare active space Hamiltonians, demonstrating that DUCC Hamiltonians provide increased accuracy without increasing the load on the quantum processor.

Figures

Figures reproduced from arXiv: 2504.18683 by the authors.

Figure 1
Figure 1. FIG. 1. Comparison of the ground states of the bare (blue squares), A4 (orange stars, dashed line), and A7 (green stars, solid [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Energy error versus the number of ADAPT-VQE iteration for LiH (8 orbital active space, 1 [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Coupled Cluster Downfolding Theory in Simulations of Chemical Systems on Quantum Hardware

    quant-ph 2025-07 accept novelty 5.0 of 10

    A coupled-cluster downfolding pipeline (QRDR) computes effective Hamiltonians in small active spaces and solves them with quantum algorithms, recovering about 98% of CCSD(T) correlation energy for benzene and free-bas...

Reference graph

Works this paper leans on

127 extracted references · 70 canonical work pages · cited by 1 Pith paper

  1. [1]

    (17) and (18), respectively)

    Potential Energy Surface Scans To evaluate the performance of DUCC downfolding, we compare the accuracy of three exact diagonalization (ED) ground state potential energy surface (PES) scans using the A4 and A7 approximations (Eqs. (17) and (18), respectively). The results of these calculations are pre- 5 sented in Fig. 1, where the left panels show absolu...

  2. [2]

    CCSD ( ˆT1=0)

    Effect of higher-body terms in the expansion The nested commutator expansions presented in Eqs. (17) and (18) include contributions that amount to three-body terms in the A4 approximation and three- and four-body terms in the A7 approximation, but these are typically neglected when forming the effective Hamil- tonians. We denote the A4 and A7 approximatio...

  3. [3]

    Feynman, R. P. Simulating physics with computers.Int. J. Theor. Phys. 1982, 21

    1982

  4. [4]

    B¨ artschi, A. et al. Potential Applications of Quantum Computing at Los Alamos National Laboratory. arXiv 2024,

    2024

  5. [5]

    D.; Love, P

    Aspuru-Guzik, A.; Dutoi, A. D.; Love, P. J.; Head- Gordon, M. Simulated quantum computation of molec- ular energies. Science 2005, 309, 1704–1707

    2005

  6. [6]

    Quantum computing in the NISQ era and beyond

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

    2018

  7. [7]

    Expanding the IBM Quantum roadmap to anticipate the future of quantum-centric supercom- puting

    Gambetta, J. Expanding the IBM Quantum roadmap to anticipate the future of quantum-centric supercom- puting. 2022; https://www.ibm.com/quantum/blog/ ibm-quantum-roadmap-2025 , Last accessed on 2024-08- 13

    2022

  8. [8]

    J.; Bluvstein, D.; Kalinowski, M.; Ebadi, S.; Manovitz, T.; Zhou, H.; Li, S

    Evered, S. J.; Bluvstein, D.; Kalinowski, M.; Ebadi, S.; Manovitz, T.; Zhou, H.; Li, S. H.; Geim, A. A.; Wang, T. T.; Maskara, N.; others High-fidelity parallel entangling gates on a neutral-atom quantum computer. Nature 2023, 622, 268–272

    2023

Show all 127 references
  1. [9]

    Shor, P. W. Scheme for reducing decoherence in quan- tum computer memory. Phys. Rev. A 1995, 52, R2493

    1995

  2. [10]

    Nature 2023, 614, 676–681

    Google Quantum AI Suppressing quantum errors by scaling a surface code logical qubit. Nature 2023, 614, 676–681

    2023

  3. [11]

    Nature 2024,

    Google Quantum AI Quantum error correction below the surface code threshold. Nature 2024,

    2024

  4. [12]

    M.; Johnson, B

    Wack, A.; Paik, H.; Javadi-Abhari, A.; Jurcevic, P.; Faro, I.; Gambetta, J. M.; Johnson, B. R. Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers. arXiv 2021,

    2021

  5. [13]

    J.; Kumph, M

    Underwood, D.; Stehlik, J.; Phung, T.; Zajac, D.; Raftery, J. J.; Kumph, M. Gate error models for super- conducting qubit architectures. Bulletin of the Ameri- can Physical Society. 2021

    2021

  6. [14]

    X.; Srinivasan, S.; Magesan, E.; Carnevale, S.; Keefe, G.; Klaus, D.; Dial, O.; McKay, D

    Kandala, A.; Wei, K. X.; Srinivasan, S.; Magesan, E.; Carnevale, S.; Keefe, G.; Klaus, D.; Dial, O.; McKay, D. Demonstration of a high-fidelity cnot gate for fixed- frequency transmons with engineered zz suppression. Phys. Rev. Lett. 2021, 127, 130501

    2021

  7. [15]

    J.; Rieffel, E

    O’Gorman, B.; Huggins, W. J.; Rieffel, E. G.; Whaley, K. B. Generalized swap networks for near- term quantum computing. arXiv preprint 2019, arXiv:1905.05118 . 11

    2019 arXiv

  8. [16]

    J.; Aspuru-Guzik, A.; O’Brien, J

    Peruzzo, A.; McClean, J.; Shadbolt, P.; Yung, M.- H.; Zhou, X.-Q.; Love, P. J.; Aspuru-Guzik, A.; O’Brien, J. L. A variational eigenvalue solver on a pho- tonic quantum processor. Nat. Commun. 2014, 5, 1–7

    2014

  9. [17]

    C.; Endo, S.; Fujii, K.; McClean, J

    Cerezo, M.; Arrasmith, A.; Babbush, R.; Ben- jamin, S. C.; Endo, S.; Fujii, K.; McClean, J. R.; Mi- tarai, K.; Yuan, X.; Cincio, L.; Coles, P. J. Variational quantum algorithms. Nat. Rev. Phys. 2021, 3, 625–644

    2021

  10. [18]

    S.; Lloyd, S

    Abrams, D. S.; Lloyd, S. Simulation of many-body Fermi systems on a universal quantum computer. Phys. Rev. Lett. 1997, 79, 2586

    1997

  11. [19]

    S.; Lloyd, S

    Abrams, D. S.; Lloyd, S. Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors. Phys. Rev. Lett. 1999, 83, 5162

    1999

  12. [20]

    R.; Romero, J.; Babbush, R.; Aspuru- Guzik, A

    McClean, J. R.; Romero, J.; Babbush, R.; Aspuru- Guzik, A. The theory of variational hybrid quantum- classical algorithms. New J. Phys. 2016, 18, 023023

    2016

  13. [21]

    M.; Gambetta, J

    Kandala, A.; Mezzacapo, A.; Temme, K.; Takita, M.; Brink, M.; Chow, J. M.; Gambetta, J. M. Hardware- efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 2017, 549, 242–246

    2017

  14. [22]

    C.; Babbush, R.; McClean, J

    Rubin, N. C.; Babbush, R.; McClean, J. Application of fermionic marginal constraints to hybrid quantum algo- rithms. New J. Phys. 2018, 20, 053020

    2018

  15. [23]

    Gokhale, P.; Angiuli, O.; Ding, Y.; Gui, K.; Tomesh, T.; Suchara, M.; Martonosi, M.; Chong, F. T. Minimizing state preparations in variational quantum eigensolver by partitioning into commuting families. arXiv preprint 2019, arXiv:1907.13623

    2019 arXiv

  16. [24]

    Pauli partition- ing with respect to gate sets

    Jena, A.; Genin, S.; Mosca, M. Pauli partition- ing with respect to gate sets. arXiv preprint 2019, arXiv:1907.07859

    2019 arXiv

  17. [25]

    F.; Yen, T.-C.; Lang, R

    Izmaylov, A. F.; Yen, T.-C.; Lang, R. A.; Vertelet- skyi, V. Unitary partitioning approach to the measure- ment problem in the variational quantum eigensolver method. J. Chem. Theory Comput. 2019, 16, 190–195

    2019

  18. [26]

    F.; Yen, T.-C.; Ryabinkin, I

    Izmaylov, A. F.; Yen, T.-C.; Ryabinkin, I. G. Revis- ing the measurement process in the variational quan- tum eigensolver: is it possible to reduce the number of separately measured operators? Chem. Sci. 2019, 10, 3746–3755

    2019

  19. [27]

    Verteletskyi, V.; Yen, T.-C.; Izmaylov, A. F. Measure- ment optimization in the variational quantum eigen- solver using a minimum clique cover. J. Chem. Phys. 2020, 152, 124114

    2020

  20. [28]

    Yen, T.-C.; Verteletskyi, V.; Izmaylov, A. F. Measur- ing all compatible operators in one series of single-qubit measurements using unitary transformations. J. Chem. Theory Comput. 2020, 16, 2400–2409

    2020

  21. [29]

    J.; McClean, J

    Huggins, W. J.; McClean, J. R.; Rubin, N. C.; Jiang, Z.; Wiebe, N.; Whaley, K. B.; Babbush, R. Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers. NPJ Quantum Inf. 2021, 7, 1–9

    2021

  22. [30]

    Predicting many properties of a quantum system from very few measure- ments

    Huang, H.-Y.; Kueng, R.; Preskill, J. Predicting many properties of a quantum system from very few measure- ments. Nat. Phys. 2020, 16, 1050–1057

    2020

  23. [31]

    C.; Miyake, A

    Zhao, A.; Rubin, N. C.; Miyake, A. Fermionic partial to- mography via classical shadows. Phys. Rev. Lett. 2021, 127, 110504

    2021

  24. [32]

    A.; Sokolov, B.; Tacchino, F.; Barkoutsos, P

    Garc´ ıa-P´ erez, G.; Rossi, M. A.; Sokolov, B.; Tacchino, F.; Barkoutsos, P. K.; Mazzola, G.; Taver- nelli, I.; Maniscalco, S. Learning to measure: Adaptive informationally complete generalized measurements for quantum algorithms. PRX quantum 2021, 2, 040342

    2021

  25. [33]

    B.; Troyer, M

    Wecker, D.; Hastings, M. B.; Troyer, M. Progress to- wards practical quantum variational algorithms. Physi- cal Review A 2015, 92, 042303

    2015

  26. [34]

    Yen, T.-C.; Ganeshram, A.; Izmaylov, A. F. Deter- ministic improvements of quantum measurements with grouping of compatible operators, non-local transfor- mations, and covariance estimates. NPJ Quantum Inf. 2023, 9, 14

    2023

  27. [35]

    D.; Coles, P

    Arrasmith, A.; Cincio, L.; Somma, R. D.; Coles, P. J. Operator sampling for shot-frugal optimization in vari- ational algorithms. arXiv preprint arXiv:2004.06252 2020,

    2004 arXiv

  28. [36]

    Efficient quantum measure- ment of Pauli operators in the presence of finite sam- pling error

    Crawford, O.; van Straaten, B.; Wang, D.; Parks, T.; Campbell, E.; Brierley, S. Efficient quantum measure- ment of Pauli operators in the presence of finite sam- pling error. Quantum 2021, 5, 385

    2021

  29. [37]

    A composite measurement scheme for efficient quantum observable estimation

    Zhang, Z.-J.; Nakaji, K.; Choi, M.; Aspuru-Guzik, A. A composite measurement scheme for efficient quantum observable estimation. arXiv preprint arXiv:2305.02439 2023,

    2023 arXiv

  30. [38]

    M.; Dub, P

    Mniszewski, S. M.; Dub, P. A.; Tretiak, S.; Anisi- mov, P. M.; Zhang, Y.; Negre, C. F. Reduction of the molecular hamiltonian matrix using quantum commu- nity detection. Sci. Rep. 2021, 11, 4099

    2021

  31. [39]

    Optimizing shot as- signment in variational quantum eigensolver measure- ment

    Zhu, L.; Liang, S.; Yang, C.; Li, X. Optimizing shot as- signment in variational quantum eigensolver measure- ment. J. Chem. Theory Comput. 2024, 20, 2390–2403

    2024

  32. [40]

    Artificial- Intelligence-Driven Shot Reduction in Quantum Mea- surement

    Liang, S.; Zhu, L.; Liu, X.; Yang, C.; Li, X. Artificial- Intelligence-Driven Shot Reduction in Quantum Mea- surement. arXiv preprint arXiv:2405.02493 2024,

    2024 arXiv

  33. [41]

    O’Malley, P. J. J. et al. Scalable quantum simulation of molecular energies. Phys. Rev. X 2016, 6, 031007

    2016

  34. [42]

    K.; Gonthier, J

    Barkoutsos, P. K.; Gonthier, J. F.; Sokolov, I.; Moll, N.; Salis, G.; Fuhrer, A.; Ganzhorn, M.; Egger, D. J.; Troyer, M.; Mezzacapo, A.; Filipp, S.; Tavernelli, I. Quantum algorithms for electronic structure calcula- tions: Particle-hole Hamiltonian and optimized wave- functio...

    2018

  35. [43]

    R.; Hempel, C.; Love, P

    Romero, J.; Babbush, R.; McClean, J. R.; Hempel, C.; Love, P. J.; Aspuru-Guzik, A. Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz. Quantum Sci. Technol. 2018, 4, 014008

    2018

  36. [44]

    I.; Ramasesh, V

    Colless, J. I.; Ramasesh, V. V.; Dahlen, D.; Blok, M. S.; Kimchi-Schwartz, M. E.; McClean, J. R.; Carter, J.; de Jong, W. A.; Siddiqi, I. Computation of molecular spectra on a quantum processor with an error-resilient algorithm. Phys. Rev. X 2018, 8, 011021

    2018

  37. [45]

    J.; Head-Gordon, M.; Wha- ley, K

    Lee, J.; Huggins, W. J.; Head-Gordon, M.; Wha- ley, K. B. Generalized unitary coupled cluster wave functions for quantum computation. J. Chem. Theory Comput. 2018, 15, 311–324

    2018

  38. [46]

    Quantum implementation of the uni- tary coupled cluster for simulating molecular electronic structure

    Shen, Y.; Zhang, X.; Zhang, S.; Zhang, J.-N.; Yung, M.- H.; Kim, K. Quantum implementation of the uni- tary coupled cluster for simulating molecular electronic structure. Phys. Rev. A 2017, 95, 020501

    2017

  39. [47]

    I.; Kevin Rhee, J.-K.; Rhee, Y

    Zhao, L.; Goings, J.; Shin, K.; Kyoung, W.; Fuks, J. I.; Kevin Rhee, J.-K.; Rhee, Y. M.; Wright, K.; Nguyen, J.; Kim, J.; Johri, S. Orbital-optimized pair-correlated elec- tron simulations on trapped-ion quantum computers. NPJ Quantum Inf. 2023, 9, 60

    2023

  40. [48]

    J.; Barkoutsos, P

    Mazzola, G.; Ollitrault, P. J.; Barkoutsos, P. K.; Taver- nelli, I. Nonunitary operations for ground-state calcula- 12 tions in near-term quantum computers. Phys. Rev. Lett. 2019, 123, 130501

    2019

  41. [49]

    Jastrow-type decomposi- tion in quantum chemistry for low-depth quantum cir- cuits

    Matsuzawa, Y.; Kurashige, Y. Jastrow-type decomposi- tion in quantum chemistry for low-depth quantum cir- cuits. J. Chem. Theory Comput. 2020, 16, 944–952

    2020

  42. [50]

    J.; Whaley, K

    Motta, M.; Sung, K. J.; Whaley, K. B.; Head- Gordon, M.; Shee, J. Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster Jastrow ansatz for electronic struc- ture. Chem. Sci. 2023, 14, 11213–11227

    2023

  43. [51]

    Robledo-Moreno, J. et al. Chemistry beyond exact so- lutions on a quantum-centric supercomputer. arXiv preprint arXiv:2405.05068 2024,

    2024 arXiv

  44. [52]

    J.; Kucharski, S

    Bartlett, R. J.; Kucharski, S. A.; Noga, J. Alternative coupled-cluster ans¨ atze II. The unitary coupled-cluster method. Chem. Phys. Lett. 1989, 155, 133–140

    1989

  45. [53]

    Error analysis and improvements of coupled-cluster theory

    Kutzelnigg, W. Error analysis and improvements of coupled-cluster theory. Theor. chim. acta 1991, 80, 349–386

    1991

  46. [54]

    Many-body problem with strong forces

    Jastrow, R. Many-body problem with strong forces. Phys. Rev. 1955, 98, 1479

    1955

  47. [55]

    G.; Yen, T.-C.; Genin, S

    Ryabinkin, I. G.; Yen, T.-C.; Genin, S. N.; Iz- maylov, A. F. Qubit coupled cluster method: a sys- tematic approach to quantum chemistry on a quantum computer. J. Chem. Theory Comput. 2018, 14, 6317– 6326

    2018

  48. [56]

    R.; Economou, S

    Grimsley, H. R.; Economou, S. E.; Barnes, E.; May- hall, N. J. An adaptive variational algorithm for ex- act molecular simulations on a quantum computer. Nat. Commun. 2019, 10, 1–9

    2019

  49. [57]

    G.; Lang, R

    Ryabinkin, I. G.; Lang, R. A.; Genin, S. N.; Iz- maylov, A. F. Iterative qubit coupled cluster approach with efficient screening of generators. J Chem. Theory Comput. 2020, 16, 1055–1063

    2020

  50. [58]

    H.; Evangelista, F

    Stair, N. H.; Evangelista, F. A. Simulating many-body systems with a projective quantum eigensolver. PRX Quantum 2021, 2, 030301

    2021

  51. [59]

    E.; Mazziotti, D

    Smart, S. E.; Mazziotti, D. A. Quantum solver of con- tracted eigenvalue equations for scalable molecular sim- ulations on quantum computing devices. Phys. Rev. Lett. 2021, 126, 070504

    2021

  52. [60]

    E.; Boyn, J.-N.; Mazziotti, D

    Smart, S. E.; Boyn, J.-N.; Mazziotti, D. A. Resolving correlated states of benzyne with an error-mitigated contracted quantum eigensolver. Phys. Rev. A 2022, 105, 022405

    2022

  53. [61]

    O.; Smart, S

    Boyn, J.-N.; Lykhin, A. O.; Smart, S. E.; Gagliardi, L.; Mazziotti, D. A. Quantum-classical hybrid algorithm for the simulation of all-electron correlation. J. Chem. Phys. 2021, 155

    2021

  54. [62]

    G.; Marti-Dafcik, D.; Tew, D

    Burton, H. G.; Marti-Dafcik, D.; Tew, D. P.; Wales, D. J. Exact electronic states with shallow quan- tum circuits from global optimisation. NPJ Quantum Inf. 2023, 9, 75

    2023

  55. [63]

    J.; Hum- ble, T

    Claudino, D.; Wright, J.; McCaskey, A. J.; Hum- ble, T. S. Benchmarking adaptive variational quantum eigensolvers. Front. Chem. 2020, 8, 1152

    2020

  56. [64]

    W.; Grimsley, H

    Bertels, L. W.; Grimsley, H. R.; Economou, S. E.; Barnes, E.; Mayhall, N. J. Symmetry breaking slows convergence of the ADAPT Variational Quantum Eigensolver. J. Chem. Theory Comput. 2022, 18, 6656– 6669

    2022

  57. [65]

    L.; Shkolnikov, V

    Tang, H. L.; Shkolnikov, V. O.; Barron, G. S.; Grims- ley, H. R.; Mayhall, N. J.; Barnes, E.; Economou, S. E. qubit-adapt-vqe: An adaptive algorithm for construct- ing hardware-efficient ans¨ atze on a quantum processor. PRX Quantum 2021, 2, 020310

    2021

  58. [66]

    S.; Armaos, V.; Barnes, C

    Yordanov, Y. S.; Armaos, V.; Barnes, C. H.; Arvidsson- Shukur, D. R. Qubit-excitation-based adaptive varia- tional quantum eigensolver. Commun. Phys. 2021, 4, 1–11

    2021

  59. [67]

    G.; Santos, L

    Ramˆ oa, M.; Anastasiou, P. G.; Santos, L. P.; May- hall, N. J.; Barnes, E.; Economou, S. E. Reducing the Resources Required by ADAPT-VQE Using Coupled Exchange Operators and Improved Subroutines. 2024

    2024

  60. [68]

    P.; Yao, Y.-X

    Gomes, N.; Mukherjee, A.; Zhang, F.; Iadecola, T.; Wang, C.-Z.; Ho, K.-M.; Orth, P. P.; Yao, Y.-X. Adap- tive variational quantum imaginary time evolution ap- proach for ground state preparation. Adv. Quantum Technol. 2021, 4, 2100114

    2021

  61. [69]

    L.; Barron, G

    Zhu, L.; Tang, H. L.; Barron, G. S.; Calderon- Vargas, F.; Mayhall, N. J.; Barnes, E.; Economou, S. E. Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum com- puter. Phys. Rev. Res. 2022, 4, 033029

    2022

  62. [70]

    Overlap-ADAPT-VQE: practical quantum chemistry on quantum computers via overlap-guided compact Ans¨ atze

    Feniou, C.; Hassan, M.; Traor´ e, D.; Giner, E.; Maday, Y.; Piquemal, J.-P. Overlap-ADAPT-VQE: practical quantum chemistry on quantum computers via overlap-guided compact Ans¨ atze. Commun. Phys. 2023, 6, 192

    2023

  63. [71]

    G.; Chen, Y.; Mayhall, N

    Anastasiou, P. G.; Chen, Y.; Mayhall, N. J.; Barnes, E.; Economou, S. E. TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ans¨ atze. Phys. Rev. Res. 2024, 6, 013254

    2024

  64. [72]

    J.; Hastings, M

    Bauer, B.; Wecker, D.; Millis, A. J.; Hastings, M. B.; Troyer, M. Hybrid quantum-classical approach to cor- related materials. Phys. Rev. X 2016, 6, 031045

    2016

  65. [73]

    C.; Jiang, Z.; Lee, E.; Bab- bush, R.; McClean, J

    Takeshita, T.; Rubin, N. C.; Jiang, Z.; Lee, E.; Bab- bush, R.; McClean, J. R. Increasing the representa- tion accuracy of quantum simulations of chemistry with- out extra quantum resources. Phys. Rev. X 2020, 10, 011004

    2020

  66. [74]

    G.; Muller, T.; Gidofalvi, G.; Lischka, H.; Shepard, R

    Szalay, P. G.; Muller, T.; Gidofalvi, G.; Lischka, H.; Shepard, R. Multiconfiguration self-consistent field and multireference configuration interaction methods and applications. Chemical reviews 2012, 112, 108–181

    2012

  67. [75]

    O.; Ya- mamoto, T.; Yan, T.; Ohnishi, Y.-y

    Mizukami, W.; Mitarai, K.; Nakagawa, Y. O.; Ya- mamoto, T.; Yan, T.; Ohnishi, Y.-y. Orbital optimized unitary coupled cluster theory for quantum computer. Phys. Rev. Res. 2020, 2, 033421

    2020

  68. [76]

    O.; Barkoutsos, P

    Sokolov, I. O.; Barkoutsos, P. K.; Ollitrault, P. J.; Greenberg, D.; Rice, J.; Pistoia, M.; Tavernelli, I. Quan- tum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algo- rithms outperform their classical equivalents? J. Chem. P...

    2020

  69. [77]

    Tilly, J.; Sriluckshmy, P.; Patel, A.; Fontana, E.; Rungger, I.; Grant, E.; Anderson, R.; Tennyson, J.; Booth, G. H. Reduced density matrix sampling: Self- consistent embedding and multiscale electronic struc- ture on current generation quantum computers. Phys. Rev. Res. 2021,...

    2021

  70. [78]

    Improving the accuracy of variational quantum eigensolvers with fewer qubits using orbital optimization

    Bierman, J.; Li, Y.; Lu, J. Improving the accuracy of variational quantum eigensolvers with fewer qubits using orbital optimization. J. Chem. Theory Comput. 2023, 19, 790–798

    2023

  71. [79]

    A.; Delcey, M

    de Gracia Trivi˜ no, J. A.; Delcey, M. G.; Wendin, G. Complete active space methods for NISQ devices: The importance of canonical orbital optimization for accu- 13 racy and noise resilience. Journal of Chemical Theory and Computation 2023, 19, 2863–2872

    2023

  72. [80]

    E.; Visscher, L

    Yalouz, S.; Senjean, B.; G¨ unther, J.; Buda, F.; O’Brien, T. E.; Visscher, L. A state-averaged orbital- optimized hybrid quantum–classical algorithm for a democratic description of ground and excited states. Quantum Sci. Technol. 2021, 6, 024004

    2021

  73. [81]

    Excited state calculations using variational quantum eigensolver with spin-restricted ans¨ atze and automatically-adjusted con- straints

    Gocho, S.; Nakamura, H.; Kanno, S.; Gao, Q.; Kobayashi, T.; Inagaki, T.; Hatanaka, M. Excited state calculations using variational quantum eigensolver with spin-restricted ans¨ atze and automatically-adjusted con- straints. NPJ Comput. Mater. 2023, 9, 13

    2023

  74. [82]

    O.; Koh, S.; Mizukami, W.; Gao, Q.; Kobayashi, T

    Omiya, K.; Nakagawa, Y. O.; Koh, S.; Mizukami, W.; Gao, Q.; Kobayashi, T. Analytical energy gradient for state-averaged orbital-optimized variational quantum eigensolvers and its application to a photochemical re- action. J. Chem. Theory Comput. 2022, 18, 741–748

    2022

  75. [83]

    W.; Lunghi, A.; Maniscalco, S.; Garc´ ıa-P´ erez, G.; Knecht, S

    Fitzpatrick, A.; Nykanen, A.; Talarico, N. W.; Lunghi, A.; Maniscalco, S.; Garc´ ıa-P´ erez, G.; Knecht, S. Self-Consistent Field Approach for the Variational Quantum Eigensolver: Orbital Optimization Goes Adaptive. J. Phys. Chem. A 2024, 128, 2843–2856

    2024

  76. [84]

    D.; Parrish, R

    Malone, F. D.; Parrish, R. M.; Welden, A. R.; Fox, T.; Degroote, M.; Kyoseva, E.; Moll, N.; Santagati, R.; Streif, M. Towards the simulation of large scale protein– ligand interactions on NISQ-era quantum computers. Chem. Sci. 2022, 13, 3094–3108

    2022

  77. [85]

    D.; Welden, A

    Loipersberger, M.; Malone, F. D.; Welden, A. R.; Parrish, R. M.; Fox, T.; Degroote, M.; Kyoseva, E.; Moll, N.; Santagati, R.; Streif, M. Accurate non- covalent interaction energies on noisy intermediate- scale quantum computers via second-order symmetry- adapted perturbation t...

    2023

  78. [86]

    E.; Rice, J

    Tammaro, A.; Galli, D. E.; Rice, J. E.; Motta, M. N-electron valence perturbation theory with reference wave functions from quantum computing: application to the relative stability of hydroxide anion and hydroxyl radical. J. Phys. Chem. A 2023, 127, 817–827

    2023

  79. [87]

    W.; Claudino, D.; Bartlett, R

    Windom, Z. W.; Claudino, D.; Bartlett, R. J. An at- tractive way to correct for missing singles excitations in unitary coupled cluster doubles theory. J. Phys. Chem. A 2024,

    2024

  80. [88]

    gold standard

    Windom, Z. W.; Claudino, D.; Bartlett, R. J. A new “gold standard”: Perturbative triples corrections in uni- tary coupled cluster theory and prospects for quantum computing. J. Chem. Phys. 2024, 160

    2024

  81. [89]

    G.; Izmaylov, A

    Ryabinkin, I. G.; Izmaylov, A. F.; Genin, S. N. A pos- teriori corrections to the iterative qubit coupled cluster method to minimize the use of quantum resources in large-scale calculations. Quantum Sci. Technol. 2021, 6, 024012

    2021

  82. [90]

    P.; Nam, Y.; Matsuura, S.; Garza, A

    Kawashima, Y.; Lloyd, E.; Coons, M. P.; Nam, Y.; Matsuura, S.; Garza, A. J.; Johri, S.; Huntington, L.; Senicourt, V.; Maksymov, A. O.; others Optimizing electronic structure simulations on a trapped-ion quan- tum computer using problem decomposition. Commun. Phys. 2021, 4, 245

    2021

  83. [91]

    Toward practi- cal quantum embedding simulation of realistic chemical systems on near-term quantum computers

    Li, W.; Huang, Z.; Cao, C.; Huang, Y.; Shuai, Z.; Sun, X.; Sun, J.; Yuan, X.; Lv, D. Toward practi- cal quantum embedding simulation of realistic chemical systems on near-term quantum computers. Chem. Sci. 2022, 13, 8953–8962

    2022

  84. [92]

    Simulating the elec- tronic structure of spin defects on quantum computers

    Huang, B.; Govoni, M.; Galli, G. Simulating the elec- tronic structure of spin defects on quantum computers. PRX Quantum 2022, 3, 010339

    2022

  85. [93]

    Quantum embedding method for the simulation of strongly correlated systems on quantum computers

    Rossmannek, M.; Pavosevic, F.; Rubio, A.; Taver- nelli, I. Quantum embedding method for the simulation of strongly correlated systems on quantum computers. J. Phys. Chem. Lett. 2023, 14, 3491–3497

    2023

  86. [94]

    P.; Rice, J

    Motta, M.; Gujarati, T. P.; Rice, J. E.; Kumar, A.; Masteran, C.; Latone, J. A.; Lee, E.; Valeev, E. F.; Takeshita, T. Y. Quantum simulation of electronic structure with a transcorrelated Hamiltonian: improved accuracy with a smaller footprint on the quantum com- puter. Phys. ...

    2020

  87. [95]

    McArdle, S.; Tew, D. P. Improving the accuracy of quantum computational chemistry using the transcorre- lated method. arXiv preprint arXiv:2006.11181 2020,

    2006 arXiv

  88. [96]

    F.; Zhang, Y.; Cincio, L.; Tretiak, S.; Dub, P

    Kumar, A.; Asthana, A.; Masteran, C.; Valeev, E. F.; Zhang, Y.; Cincio, L.; Tretiak, S.; Dub, P. A. Quan- tum simulation of molecular electronic states with a transcorrelated Hamiltonian: higher accuracy with fewer qubits. J. Chem. Theory Comput. 2022, 18, 5312– 5324

    2022

  89. [97]

    S.; Aspuru-Guzik, A

    Schleich, P.; Kottmann, J. S.; Aspuru-Guzik, A. Im- proving the accuracy of the variational quantum eigen- solver for molecular systems by the explicitly-correlated perturbative [2] R12-correction. Phys. Chem. Chem. Phys. 2022, 24, 13550–13564

    2022

  90. [98]

    O.; Liao, K.; R´ ıos, P

    Dobrautz, W.; Sokolov, I. O.; Liao, K.; R´ ıos, P. L.; Rahm, M.; Alavi, A.; Tavernelli, I. Toward Real Chem- ical Accuracy on Current Quantum Hardware Through the Transcorrelated Method. J. Chem. Theory Comput. 2024,

    2024

  91. [99]

    Towards efficient quantum computing for quantum chemistry: Reducing circuit complexity with transcorrelated and adaptive ansatz techniques

    Magnusson, E.; Fitzpatrick, A.; Knecht, S.; Rahm, M.; Dobrautz, W. Towards efficient quantum computing for quantum chemistry: Reducing circuit complexity with transcorrelated and adaptive ansatz techniques. Farad. Disc. 2024,

    2024

  92. [100]

    Kowalski, K.; Bauman, N. P. Fock-Space Schrief- fer–Wolff Transformation: Classically-Assisted Rank- Reduced Quantum Phase Estimation Algorithm. Ap- plied Sciences 2023, 13

    2023

  93. [101]

    Quantum algo- rithms for Schrieffer-Wolff transformation

    Zhang, Z.; Yang, Y.; Xu, X.; Li, Y. Quantum algo- rithms for Schrieffer-Wolff transformation. Phys. Rev. Res. 2022, 4, 043023

    2022

  94. [102]

    Huang, R.; Li, C.; Evangelista, F. A. Leveraging small- scale quantum computers with unitarily downfolded hamiltonians. PRX Quantum 2023, 4, 020313

    2023

  95. [103]

    Properties of coupled-cluster equations originating in excitation sub-algebras

    Kowalski, K. Properties of coupled-cluster equations originating in excitation sub-algebras. J. Chem. Phys. 2018, 148

    2018

  96. [104]

    Sub-system self-consistency in coupled cluster theory

    Kowalski, K. Sub-system self-consistency in coupled cluster theory. J. Chem. Phys. 2023, 158

    2023

  97. [105]

    P.; Bylaska, E

    Bauman, N. P.; Bylaska, E. J.; Krishnamoorthy, S.; Low, G. H.; Wiebe, N.; Granade, C. E.; Roetteler, M.; Troyer, M.; Kowalski, K. Downfolding of many-body Hamiltonians using active-space models: Extension of the sub-system embedding sub-algebras approach to unitary coupled clu...

    2019

  98. [106]

    P.; Low, G

    Bauman, N. P.; Low, G. H.; Kowalski, K. Quantum sim- ulations of excited states with active-space downfolded Hamiltonians. J. Chem. Phys. 2019, 151. 14

    2019

  99. [107]

    P.; Peng, B.; Kowalski, K

    Bauman, N. P.; Peng, B.; Kowalski, K. Coupled Clus- ter Green’s function formulations based on the effective Hamiltonians. Mol. Phys. 2020, 118, e1725669

    2020

  100. [108]

    Kowalski, K.; Bauman, N. P. Sub-system quantum dy- namics using coupled cluster downfolding techniques. J. Chem. Phys. 2020, 152

    2020

  101. [109]

    P.; Kowalski, K.; De Jong, W

    Metcalf, M.; Bauman, N. P.; Kowalski, K.; De Jong, W. A. Resource-efficient chemistry on quantum computers with the variational quantum eigensolver and the double unitary coupled-cluster approach. J. Chem. Theory Comput. 2020, 16, 6165–6175

    2020

  102. [110]

    Bauman, N. P.; Chl´ adek, J.; Veis, L.; Pittner, J.; Karol, K.; others Variational quantum eigensolver for approximate diagonalization of downfolded hamiltoni- ans using generalized unitary coupled cluster ansatz. Quantum Sci. Technol. 2021, 6, 034008

    2021

  103. [111]

    P.; Kowalski, K

    Bauman, N. P.; Kowalski, K. Coupled cluster downfold- ing theory: towards universal many-body algorithms for dimensionality reduction of composite quantum sys- tems in chemistry and materials science. Mater. Theory 2022, 6, 17

    2022

  104. [112]

    P.; Kowalski, K

    Bauman, N. P.; Kowalski, K. Coupled cluster downfold- ing methods: The effect of double commutator terms on the accuracy of ground-state energies. J. Chem. Phys. 2022, 156

    2022

  105. [113]

    P.; Peng, B.; Kowalski, K

    Bauman, N. P.; Peng, B.; Kowalski, K. Coupled-cluster downfolding techniques: A review of existing applica- tions in classical and quantum computing for chemical systems. Advances in Quantum Chemistry 2023, 87, 141–166

    2023

  106. [114]

    Kowalski, K.; Bauman, N. P. Quantum flow algorithms for simulating many-body systems on quantum comput- ers. Phys. Rev. Lett. 2023, 131, 200601

    2023

  107. [115]

    S.; Arvidsson-Shukur, D

    Yordanov, Y. S.; Arvidsson-Shukur, D. R. M.; Barnes, C. H. W. Efficient quantum circuits for quan- tum computational chemistry. Phys. Rev. A 2020, 102, 062612

    2020

  108. [116]

    O.; Mayhall, N

    Shkolnikov, V. O.; Mayhall, N. J.; Economou, S. E.; Barnes, E. Avoiding symmetry roadblocks and minimiz- ing the measurement overhead of adaptive variational quantum eigensolvers. Quantum 2023, 7, 1040

    2023

  109. [117]

    A.; Chan, G

    Evangelista, F. A.; Chan, G. K.-L.; Scuseria, G. E. Ex- act parameterization of fermionic wave functions via unitary coupled cluster theory. J. Chem. Phys. 2019, 151, 244112

    2019

  110. [118]

    D.; Bartlett, R

    Purvis III, G. D.; Bartlett, R. J. A full coupled-cluster singles and doubles model: The inclusion of discon- nected triples. J. Chem. Phys. 1982, 76, 1910–1918

    1982

  111. [119]

    Møller, C.; Plesset, M. S. Note on an Approxima- tion Treatment for Many-Electron Systems. Phys. Rev. 1934, 46, 618–622

    1934

  112. [120]

    C.; Blunt, N

    Sun, Q.; Berkelbach, T. C.; Blunt, N. S.; Booth, G. H.; Guo, S.; Li, Z.; Liu, J.; McClain, J. D.; Sayfut- yarova, E. R.; Sharma, S.; Wouters, S.; Chan, G. K.- L. PySCF: the Python-based simulations of chemistry framework. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2018, 8, e1340

    2018

  113. [121]

    Sun, Q. et al. Recent developments in the PySCF pro- gram package. J. Chem. Phys. 2020, 153, 024109

    2020

  114. [122]

    Dunning, T. H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 1989, 90, 1007– 1023

    1989

  115. [123]

    P.; Woon, D

    Prascher, B. P.; Woon, D. E.; Peterson, K. A.; Dun- ning, T. H.; Wilson, A. K. Gaussian basis sets for use in correlated molecular calculations. VII. Valence, core- valence, and scalar relativistic basis sets for Li, Be, Na, and Mg. Theor. Chem. Acc. 2011, 128, 69–82

    2011

  116. [124]

    Evangelista, F. A. Automatic derivation of many-body theories based on general Fermi vacua. The Journal of Chemical Physics 2022, 157, 064111

    2022

  117. [125]

    McClean, J. R. et al. OpenFermion: the electronic struc- ture package for quantum computers. Quantum Sci. Technol. 2020, 5, 034014

    2020

  118. [126]

    B.; Tubman, N

    Williams-Young, D. B.; Tubman, N. M.; Mejuto- Zaera, C.; de Jong, W. A. A parallel, distributed mem- ory implementation of the adaptive sampling config- uration interaction method. The Journal of Chemical Physics 2023, 158, 214109

    2023

  119. [127]

    J.; Taylor, P

    Lee, T. J.; Taylor, P. R. A diagnostic for determining the quality of single-reference electron correlation methods. Int. J. Quantum Chem. 1989, 36, 199–207. Supporting Information: Qubit-efficient quantum chemistry with the ADAPT variational quantum eigensolver and double uni...

    1989 arXiv

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.