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This paper predicts that quantum phase estimation will first beat full configuration interaction around 2032, while DFT never becomes quantum-advantageous.

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Factoring in hardware overheads and error correction, the authors predict classical chemistry algorithms stay dominant for most calculations through the 2040s, while quantum phase estimation overtakes full configuration interaction and high-order coupled cluster for small-to-medium molecules in the

T0 review reviewed 2026-08-05 challenge →

load-bearing objection A transparent, well-caveated forecasting paper whose qualitative conclusions hold up, but whose year-specific numbers rest on a single unvalidated overhead anchor that the robustness study never perturbs. the 3 major comments →

arxiv 2508.20972 v1 pith:J4NL23SF submitted 2025-08-28 quant-ph

Quantum Advantage in Computational Chemistry?

Hans Gundlach , Keeper Sharkey , Jayson Lynch , Victoria Hazoglou , Kung-Chuan Hsu , Carl Dukatz , Eleanor Crane , Karin Walczyk
show 3 more authors
Marcin Bodziak Johannes Galatsanos-Dueck Neil Thompson
This is my paper
classification quant-ph
keywords quantum computational chemistryquantum phase estimationquantum economic advantagefault-tolerant quantum computingfull configuration interactioncoupled cluster methodsdensity functional theoryresource estimation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The reading

The paper takes the standard promise that quantum computers will revolutionize computational chemistry and asks when a fault-tolerant machine would actually be cheaper and faster than a classical one once hardware price, error correction, and gate speeds are included. Extending a cost-comparison framework, it finds that quantum phase estimation will first beat full configuration interaction around 2032, then the coupled-cluster methods CCSD(T) and CCSD in the 2030s and 2040s. Workhorse methods—density functional theory, Hartree–Fock, and Møller–Plesset—remain classical choices for at least two more decades, with DFT never reaching quantum economic advantage. The near-term impact of quantum computing in chemistry is therefore a niche: highly accurate calculations on small-to-medium strongly correlated molecules, not a general replacement for the field.

Core claim

The paper's central claim is that, once hardware economics are included, the asymptotic advantage of quantum phase estimation is overwhelmed by a roughly 10^13 price-comparable classical speed advantage for all but the most expensive classical methods. It computes crossover problem sizes for six standard methods and maps them against projected qubit counts and a one-month runtime limit. Under a cubic-scaling QPE assumption, the first year of quantum economic advantage is 2032 for full configuration interaction, 2036 for CCSD(T), 2044 for CCSD, beyond 2050 for Hartree–Fock and Møller–Plesset, and never for density functional theory. Under optimistic O(N^2/ϵ) QPE implementations these years mo

What carries the argument

The load-bearing object is the quantum economic advantage threshold: the smallest problem size at which a quantum algorithm's better asymptotic scaling overcomes a fixed hardware overhead, set by the paper at about 10^13 price-comparable operations per dollar. For each classical method with cost C(N), the crossover is found by solving C(N) ≈ 10^13 Q(N), where Q(N) is the QPE cost; the paper then checks whether the resulting molecule size can be run within a month on the number of logical qubits projected from hardware roadmaps. This threshold is what separates methods like FCI, whose exponential classical cost crosses quickly, from DFT, whose cubic cost never crosses.

Load-bearing premise

The timeline rests on the assumption that fault-tolerant quantum computing inherits today's prototype price gap, so a quantum machine costs about 10^13 times more per useful operation than a GPU-equipped classical machine; if error-corrected quantum gates get relatively cheaper against GPUs, every predicted advantage year moves earlier.

What would settle it

Take the first fault-tolerant machine with 50-100 logical qubits, run quantum phase estimation on a molecule with roughly 50 basis functions, and compare dollar cost and wall-clock time against the best classical FCI or CCSD(T) calculation at the same target accuracy. If the quantum run finishes in under a month and under the classical cost before 2032, the paper's central timeline is wrong; if a 2040s-era machine still cannot beat classical on a 300-orbital strongly correlated system, the optimistic branch fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the central prediction holds, the first commercially relevant quantum chemistry applications appear around 2032, replacing full configuration interaction for small-to-medium strongly correlated molecules rather than routine calculations.
  • Quantum computing will not dislodge density functional theory on economic grounds, because a cubic-scaling classical method and a cubic-scaling QPE leave the 10^13 hardware overhead unresolved; classical DFT remains the default for large systems.
  • For the gold-standard method CCSD(T), the crossover comes in the mid-2030s, meaning quantum estimates of perturbative triples become cost-competitive before routine MP2 or Hartree–Fock do.
  • If QPE implementations reach O(N^2/ϵ) scaling as some recent algorithms suggest, the first advantage years move to the early 2030s and systems around 10^5 atoms become feasible in the 2040s.
  • Under older O(N^5) QPE implementations, only FCI is ever surpassed, and at small sizes (fewer than about 10 atoms), showing how much the timeline depends on algorithmic progress rather than hardware alone.

Where Pith is reading between the lines

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

  • I read the 'never for DFT' result as economic rather than algorithmic: a quantum DFT algorithm with a constant-factor speedup could still be useful, but the framework's fixed 10^13 overhead means the crossover never occurs at any molecule size.
  • Because the 10^13 overhead is a ratio of classical to quantum cost, the predicted years respond symmetrically to hardware progress: a slowdown in classical performance would bring every Table I year earlier by roughly the same amount, even with zero quantum improvement.
  • The paper explicitly sets aside AI-based classical surrogates; if learned interatomic potentials replace first-principles calculations for routine chemistry before 2032, the niche where quantum advantage first appears may also shrink.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This paper applies the "quantum tortoise and classical hare" framework of Choi, Moses, and Thompson to computational chemistry. It compares the asymptotic costs of classical methods (DFT, HF, MP2, CCSD, CCSD(T), FCI) with fault-tolerant quantum phase estimation (QPE) at three assumed scalings (O(N^5/eps), O(N^3/eps), O(N^2/eps)), folds in a price-comparable overhead of ~10^13 derived in Appendix A, models qubit availability by extrapolating IBM's roadmap, and imposes a one-month wall-clock feasibility limit. The headline outputs are Table I's first-year-of-quantum-economic-advantage predictions: 2032 for FCI and 2034/2036 for CCSD(T) under cubic-scaling QPE, with CCSD at 2044, MP2/HF beyond 2050, and DFT never. The authors conclude that quantum computers will be impactful for small-to-medium strongly correlated systems in the next decade or so, while routine computational chemistry remains classical for at least two decades.

Significance. If the quantitative framework were sound, this would be a valuable, field-level assessment that moves beyond single-molecule resource estimates. The paper is honest about several limitations (state preparation, QMA-hardness, basis-set dependence) and makes an effort to benchmark its constants against external resource estimates (Tables IV and V). The robustness study, while incomplete, is a genuine contribution. The main conclusions are falsifiable and clearly stated. However, the year predictions are only as credible as the unvalidated 10^13 price-comparable overhead, and because the hardware-evolution model is deferred to an unpublished working paper [49], the central quantitative claims are not yet checkable from the manuscript. With the baseline assumption made explicit and stress-tested, this could become an influential reference.

major comments (3)
  1. [Sec. II-C and Appendix A] The 10^13 price-comparable overhead controls every year in Table I, yet its 10^8 component is anchored to 2025 NISQ cloud prices ($1.3–$1.6/s vs $0.001/s H100). No argument shows this ratio persists for fault-tolerant machines, and the detailed hardware model is deferred to [49], so Table I is not reproducible from the text. Table II varies algorithmic constants by 10x but never varies the 10^13 baseline. If FT per-logical-gate cost drops 100x, C→10^11; a 10^4 drop gives C≈10^9 and threatens the two-decade conclusion. Add a sensitivity analysis over C or include the model.
  2. [Appendix A] The decomposition of the 10^13 overhead is not transparent. The 10^8 residual is attributed to 'increased parallelism caused by the quantum-classical price difference,' but the quoted price ratio is only 10^3 (1000 H100 GPU-seconds per dollar vs ~0.7 quantum seconds per dollar). The step from price difference and H100 tensor-core parallelism to 10^8 is not shown. Because this is the largest component of C, the derivation must be explicit (FLOPs per H100, logical gates per quantum processor, price ratio) or the numerical anchor is unverifiable.
  3. [Sec. II-B and Sec. III-A] The paper acknowledges O(1/F) repetition cost and QMA-hardness, then assumes efficient state preparation. This assumption directly inflates the effective T-gate count. The robustness study (Table II) has no row for state-preparation overhead (e.g., the factor of ~2 estimated for FeMoco in [28] or a 10x poor-overlap case). Since the 'coming decade' FCI/CCSD(T) claims are at stake, please add such a row or provide quantitative evidence that F is close to 1 for the target systems.
minor comments (6)
  1. [Abstract vs Table I] MP2 is said to be 'surpassed in ten to fifteen years,' but this only holds for the optimistic O(N^2/eps) QPE scaling; under the default O(N^3/eps) Table I gives >2050. Please qualify.
  2. [Abstract] 'Tens or hundreds of atoms' is too broad for FCI. With the paper's crossover condition and orbital-to-atom ratios, FCI advantage occurs at N≈30 basis functions, i.e., a handful of atoms; CCSD(T) is the method that reaches hundreds of atoms. Clarify.
  3. [Sec. II-A3 / Table IV] The text says classical time estimates are 0.1–1 times naive, but the CCSD(T) entry is 1.67, contradicting that range.
  4. [Typos] 'Couple Cluster' in Table I; 'numerious variations and adaptations in implimentation details' in Sec. II-A3; 'Sterling Approximation' should be Stirling; 'Mølybdemun' in the Introduction.
  5. [Fig. 2] The 'maximum theoretical size' with no time limit is unclear; state explicitly that it is bounded by qubit count/error-correction overhead, not by time.
  6. [Sec. III-A / III-B] 'Mostly robust to increased overhead of about an order of magnitude' is overstated. For CCSD with N^3 QPE, a 10x quantum-time increase moves the first year from 2044 to >2050. Please temper the robustness summary.

Circularity Check

2 steps flagged

Load-bearing self-citations carry the 10^13 overhead and its time evolution, but chemistry-specific comparisons are independently grounded; score 4.

specific steps
  1. self citation load bearing [Section II-C, paragraph on hardware trend modeling (after Fig. 2)]
    "The detailed estimates for these improvements in quantum hardware and our model of quantum overhead we include in forthcoming work [49]."

    Table I's advantage years and Fig. 2's QEA lines are computed by evolving the 10^13 overhead over time; that time evolution is not specified in this paper but deferred to [49], an unpublished working paper with overlapping authorship (Gundlach, Lynch, Thompson). The headline 'classical superiority for two decades' therefore rests on a self-citation whose content is not checkable from the present text, satisfying the load-bearing self-citation pattern.

  2. self citation load bearing [Appendix A, 'How Fast Are Quantum Computers?']
    "We adopt the initial classical and quantum speed difference as established in [1]. By default, we set the classical clock speed at 5 GHz and the quantum clock speed at 2 MHz. The error correction overhead is assumed to introduce a slowdown on the order of 10^2 for each logical operation [1]."

    The 10^5 naive slowdown is one of the two multiplicative factors in the 10^13 overhead (10^5 × 10^8) that determines every crossover threshold. Rather than deriving this factor, the paper imports it from [1], which shares author N. Thompson. Combined with [49]'s deferred hardware evolution, the central numeric result is inherited from the same authors' prior and forthcoming work; the chemistry-specific comparisons and external T-gate benchmarks provide independent content not present in [1].

full rationale

The paper's core crossover condition is C·N^3 < N^p with C≈10^13 (Sec. II-C). That constant is not fitted to the target years; it is assembled in Appendix A from clock speeds, a 10^2 error-correction factor, and 2025 cloud prices. The chemistry side is independently grounded: asymptotic scalings for DFT/HF/MP2/CCSD/CCSD(T)/FCI are standard, QPE T-gate estimates are checked against external resource studies [12,51], and robustness analysis varies quantum/classical constants. However, two load-bearing pieces are inherited from self-citations. First, the 10^5 naive quantum slowdown is taken from [1], whose author list overlaps (N. Thompson). Second, the time-evolution of the overhead—what actually produces the advantage years in Table I—is deferred entirely to [49], an unpublished working paper by three of the present authors. Thus the headline 'classical superiority for two decades' depends for its quantitative content on the authors' own prior/forthcoming work rather than on a derivation in this paper. That is not full circularity (the chemistry predictions are not fitted to a target, and external benchmarks play a role), but it is a material self-citation load-bearing on the central result.

Axiom & Free-Parameter Ledger

9 free parameters · 6 axioms · 0 invented entities

The central claim rests on roughly nine chosen inputs (overhead differential, QPE scaling exponent, qubit overhead factor, classical constants, growth rates, roadmap extrapolation, time limit, error tolerance, orbital-to-atom ratios), the classical and quantum asymptotic complexity axioms, and favorable state-preparation and hardware assumptions. No invented physical entities are introduced; 'Quantum Economic Advantage problem size' is a derived metric, not an entity.

free parameters (9)
  • quantum_classical_price_comparable_speed_differential = 10^13 = 10^5 (naive slowdown) x 10^8 (price/parallelism)
    Inherited from Choi-Moses-Thompson [1]; anchors every QEA crossover. The 10^8 component derives from 2025 cloud prices for NISQ machines vs H100 GPUs (Appendix A); assumed to persist for fault-tolerant machines.
  • QPE_asymptotic_scaling_exponent = 3 (default), 2 (optimistic), 5 (historical)
    The paper chooses O(N^3/epsilon) as its main estimate from surveyed implementations (Section II-B); this choice directly sets all advantage years in Table I.
  • logical_qubit_overhead_factor = 10x basis-function count
    Averaged from Otten et al. [51] and Nguyen et al. [10] resource estimates (Section II-C.1).
  • classical_algorithmic_constants = c_alg = 0.12 (CCSD), 1.67 (CCSD(T)), 1.0 assumed for others
    Fit from two GPU benchmarks (Table IV); other methods assumed at roughly 1.0, acknowledged as under-constrained.
  • classical_performance_growth_rate = 40% per year
    Moore's-law assumption for classical hardware improvement (Section II-C).
  • qubit_roadmap_extrapolation = exponential fit to IBM's 2024 roadmap
    Sets the feasibility limit for QPE; provider roadmaps extrapolated exponentially (Section II-C).
  • one_month_wallclock_limit = 30 days
    Practical time limit chosen for chemistry use cases; shifts which problem sizes are feasible (Section II-C).
  • error_tolerance = epsilon = 10^-3 Hartree
    Convention from [7] and [52]; multiplies QPE runtime through the 1/epsilon factor.
  • orbital_to_atom_ratio = 16.8 (FeMoco mixed basis), 4.3 (hydrocarbons)
    Converts basis-function counts into atom counts for readability (Appendix B); affects the abstract's 'tens or hundreds of atoms' framing.
axioms (6)
  • domain assumption Representative classical asymptotic complexities: DFT O(N^3), HF O(N^4), MP2 O(N^5), CCSD O(N^6), CCSD(T) O(N^7), FCI O*(4^N) in N basis functions
    Taken from literature (Sections II-A.1 to II-A.3); the paper states these are representative, not the most optimized implementations.
  • domain assumption QPE with qubitization has ~O(N^3/epsilon) T-gate complexity for chemistry Hamiltonians in typical regimes
    Chosen from surveyed implementations ([23], [25], Section II-B); the paper flags it may be optimistic.
  • domain assumption Initial state preparation can be done efficiently with high overlap (F ~ 1), so QPE repetition cost O(1/F) is negligible
    Stated in Section II-B; the paper notes a factor-of-2 overhead for FeMoco [28] and the QMA-hardness caveat but proceeds with favorable assumptions.
  • domain assumption Surface-code error correction adds ~10^2 slowdown per logical operation, with physical-to-logical qubit ratios from cited literature
    Section II-C and Appendix A; the detailed trend model for how this ratio improves over time is deferred to [49].
  • domain assumption Superconducting hardware with ~2 MHz physical gate clock vs classical 5 GHz GPU clock
    Appendix A, carried from [1]; gate speed is a key driver of the 10^13 overhead.
  • domain assumption FCI cost is bounded by the half-filled binomial coefficient C(2N,N) ~ 4^N
    Section II-A.3 uses Stirling approximation at half-filling (Ne = N spatial orbitals) as an upper bound on the FCI determinant count.

reviewed 2026-08-05 · how reviews work

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Cite this review

Pith. "Pith review of Quantum Advantage in Computational Chemistry?." pith.science (2026). https://pith.science/paper/J4NL23SF

@misc{pith2026250820972,
  author       = {Pith},
  title        = {Pith review of: Quantum Advantage in Computational Chemistry?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J4NL23SF}},
  note         = {Machine review of arXiv:2508.20972}
}
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read the original abstract

For decades, computational chemistry has been posited as one of the areas in which quantum computing would revolutionize. However, the algorithmic advantages that fault-tolerant quantum computers have for chemistry can be overwhelmed by other disadvantages, such as error correction, processor speed, etc. To assess when quantum computing will be disruptive to computational chemistry, we compare a wide range of classical methods to quantum computational methods by extending the framework proposed by Choi, Moses, and Thompson. Our approach accounts for the characteristics of classical and quantum algorithms, and hardware, both today and as they improve. We find that in many cases, classical computational chemistry methods will likely remain superior to quantum algorithms for at least the next couple of decades. Nevertheless, quantum computers are likely to make important contributions in two important areas. First, for simulations with tens or hundreds of atoms, highly accurate methods such as Full Configuration Interaction are likely to be surpassed by quantum phase estimation in the coming decade. Secondly, in cases where quantum phase estimation is most efficient less accurate methods like Couple Cluster and Moller-Plesset, could be surpassed in fifteen to twenty years if the technical advancements for quantum computers are favorable. Overall, we find that in the next decade or so, quantum computing will be most impactful for highly accurate computations with small to medium-sized molecules, whereas classical computers will likely remain the typical choice for calculations of larger molecules.

Figures

Figures reproduced from arXiv: 2508.20972 by Carl Dukatz, Eleanor Crane, Hans Gundlach, Jayson Lynch, Johannes Galatsanos-Dueck, Karin Walczyk, Keeper Sharkey, Kung-Chuan Hsu, Marcin Bodziak, Neil Thompson, Victoria Hazoglou.

Figure 2
Figure 2. Figure 2: QEA threshold problem size over time taking into [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Evaluation of when quantum computing could replace select quantum chemistry algorithms at certain sizes. The region [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Reference graph

Works this paper leans on

68 extracted references · 44 canonical work pages · cited by 2 Pith papers

  1. [1]

    The quantum tortoise and the classical hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not),

    S. Choi, W. S. Moses, and N. Thompson, “The quantum tortoise and the classical hare: A simple framework for understanding which problems quantum computing will accelerate (and which it will not),”

    work page

  2. [2]

    The top 100 papers,

    Nature Editors, “The top 100 papers,” Nature, 2005, accessed: 25 March 2025. [Online]. Available: https://www.nature.com/news/the-top- 100-papers-1.16224

    work page 2005

  3. [3]

    Elucidating reaction mechanisms on quantum computers,

    M. Reiher, N. Wiebe, K. M. Svore, D. Wecker, and M. Troyer, “Elucidating reaction mechanisms on quantum computers,” Proceedings of the National Academy of Sciences , vol. 114, no. 29, pp. 7555–7560,

    work page

  4. [4]

    Drug design on quantum computers,

    R. Santagati, A. Aspuru-Guzik, R. Babbush, M. Degroote, L. Gonz ´alez, E. Kyoseva, N. Moll, M. Oppel, R. M. Parrish, N. C. Rubin et al. , “Drug design on quantum computers,” Nature Physics , vol. 20, no. 4, pp. 549–557, 2024

    work page 2024

  5. [5]

    Reliably assessing the electronic structure of cytochrome p450 on today’s classical computers and tomorrow’s quantum computers,

    J. J. Goings, A. White, J. Lee, C. S. Tautermann, M. Degroote, C. Gidney, T. Shiozaki, R. Babbush, and N. C. Rubin, “Reliably assessing the electronic structure of cytochrome p450 on today’s classical computers and tomorrow’s quantum computers,” Proceedings of the National Academy of Sciences , vol. 119, no. 38, Sep. 2022. [Online]. Available: http://dx.d...

    doi:10.1073/pnas.2203533119 2022

  6. [6]

    Elucidating reaction mechanisms on quantum computers,

    M. Reiher, N. Wiebe, K. M. Svore, D. Wecker, and M. Troyer, “Elucidating reaction mechanisms on quantum computers,” Proceedings of the National Academy of Sciences , vol. 114, no. 29, p. 7555–7560, Jul

    work page

  7. [7]

    Phase estimation with partially randomized time evolution,

    J. G ¨unther, F. Witteveen, A. Schmidhuber, M. Miller, M. Christandl, and A. Harrow, “Phase estimation with partially randomized time evolution,” 2025. [Online]. Available: https://arxiv.org/abs/2503.05647

    Pith/arXiv arXiv 2025

  8. [8]

    Feasibility of accelerating homogeneous catalyst discovery with fault-tolerant quantum computers,

    N. Bellonzi, A. Kunitsa, J. T. Cantin, J. A. Campos-Gonzalez-Angulo, M. D. Radin, Y . Zhou, P. D. Johnson, L. A. Mart ´ınez-Mart´ınez, M. R. Jangrouei, A. S. Brahmachari, L. Wang, S. Patel, M. Kodrycka, I. Loaiza, R. A. Lang, A. Aspuru-Guzik, A. F. Izmaylov, J. R. Fontalvo, and Y . Cao, “Feasibility of accelerating homogeneous catalyst discovery with faul...

    arXiv 2024

  9. [9]

    Available: http://dx.doi.org/10.1073/pnas.1619152114

    [Online]. Available: http://dx.doi.org/10.1073/pnas.1619152114

    doi:10.1073/pnas.1619152114

  10. [10]

    Quantum computing for corrosion-resistant materials and anti-corrosive coatings design,

    N. Nguyen, T. W. Watts, B. Link, K. S. Williams, Y . R. Sanders, S. J. Elman, M. Kieferova, M. J. Bremner, K. J. Morrell, J. Elenewski, E. B. Isaacs, S. D. Johnson, L. Mathieson, K. M. Obenland, M. Otten, R. Sundareswara, and A. Holmes, “Quantum computing for corrosion-resistant materials and anti-corrosive coatings design,” 2024. [Online]. Available: htt...

    Pith/arXiv arXiv 2024

  11. [11]

    Quantum resources required for binding affinity calculations of amyloid beta,

    M. Otten, T. W. Watts, S. D. Johnson, R. Sundareswara, Z. Wang, T. S. Hardikar, K. Heitritter, J. Brown, K. Setia, and A. Holmes, “Quantum resources required for binding affinity calculations of amyloid beta,”

    work page

  12. [12]

    Quantifying fault tolerant simulation of strongly correlated systems using the fermi-hubbard model,

    A. A. Agrawal, J. Job, T. L. Wilson, S. N. Saadatmand, M. J. Hodson, J. Y . Mutus, A. Caesura, P. D. Johnson, J. E. Elenewski, K. J. Morrell, and A. F. Kemper, “Quantifying fault tolerant simulation of strongly correlated systems using the fermi-hubbard model,” 2024. [Online]. Available: https://arxiv.org/abs/2406.06511

    Pith/arXiv arXiv 2024

  13. [13]

    Implementation of the density-functional theory on quantum computers with linear scaling with respect to the number of atoms,

    T. Ko, X. Li, and C. Wang, “Implementation of the density-functional theory on quantum computers with linear scaling with respect to the number of atoms,” arXiv preprint arXiv:2307.07067 , 2023

    Pith/arXiv arXiv 2023

  14. [14]

    Accurate and efficient linear scaling dft calculations with universal applicability,

    S. Mohr, L. E. Ratcliff, L. Genovese, D. Caliste, P. Boulanger, S. Goedecker, and T. Deutsch, “Accurate and efficient linear scaling dft calculations with universal applicability,” Physical Chemistry Chemical Physics, vol. 17, no. 47, pp. 31 360–31 370, 2015

    work page 2015

  15. [15]

    5.61 f17 lecture 29: Modern electronic structure theory: Electron correlation,

    R. Field, “5.61 f17 lecture 29: Modern electronic structure theory: Electron correlation,” 2017, lecture Notes, Fall

    work page 2017

  16. [16]

    Fast quantum simulation of electronic structure by spectrum amplification,

    G. H. Low, R. King, D. W. Berry, Q. Han, A. E. D. III, A. White, R. Babbush, R. D. Somma, and N. C. Rubin, “Fast quantum simulation of electronic structure by spectrum amplification,” 2025. [Online]. Available: https://arxiv.org/abs/2502.15882

    arXiv 2025

  17. [17]

    Linear-scaling implementation of multilevel hartree–fock theory,

    L. Goletto, E. F. Kjønstad, S. D. Folkestad, I.-M. Høyvik, and H. Koch, “Linear-scaling implementation of multilevel hartree–fock theory,” Jour- nal of Chemical Theory and Computation , vol. 17, no. 12, pp. 7416– 7427, 2021

    work page 2021

  18. [18]

    Rank- reduced coupled-cluster. iii. tensor hypercontraction of the doubles amplitudes,

    E. G. Hohenstein, B. S. Fales, R. M. Parrish, and T. J. Mart ´ınez, “Rank- reduced coupled-cluster. iii. tensor hypercontraction of the doubles amplitudes,” The Journal of Chemical Physics , vol. 156, no. 5, 2022

    work page 2022

  19. [19]

    J. D. Watts, J. Gauss, and R. J. Bartlett, “Coupled-cluster methods with noniterative triple excitations for restricted open-shell hartree–fock and other general single determinant reference functions. energies and analytical gradients,” The Journal of chemical physics , vol. 98, no. 11, pp. 8718–8733, 1993

    work page 1993

  20. [20]

    Available: https://ocw.mit.edu/courses/5-61-physical- chemistry-fall-2017/resources/mit5 61f17 lec29/

    [Online]. Available: https://ocw.mit.edu/courses/5-61-physical- chemistry-fall-2017/resources/mit5 61f17 lec29/

    work page 2017

  21. [21]

    Hartree–Fock and post–Hartree–Fock methods: Computational aspects,

    P. Loos, “Hartree–Fock and post–Hartree–Fock methods: Computational aspects,” Lecture Notes, ISTPC Summer School, Jun. 2022, accessed: 2025-04-12. [Online]. Available: https://lcqs.unistra.fr/wordpress/wp- content/uploads/dlm uploads/2023/12/istpc loos posthf.pdf

    work page 2022

  22. [22]

    The density-matrix renormalization group,

    U. Schollw ¨ock, “The density-matrix renormalization group,” Reviews of modern physics , vol. 77, no. 1, pp. 259–315, 2005

    work page 2005

  23. [23]

    Even more efficient quantum computations of chemistry through tensor hypercontraction,

    J. Lee, D. W. Berry, C. Gidney, W. J. Huggins, J. R. McClean, N. Wiebe, and R. Babbush, “Even more efficient quantum computations of chemistry through tensor hypercontraction,” PRX Quantum , vol. 2, no. 3, Jul. 2021. [Online]. Available: http://dx.doi.org/10.1103/ PRXQuantum.2.030305

    work page 2021

  24. [24]

    Quantum simulation of chemistry with sublinear scaling in basis size,

    R. Babbush, D. W. Berry, J. R. McClean, and H. Neven, “Quantum simulation of chemistry with sublinear scaling in basis size,” npj Quantum Information , vol. 5, no. 1, Nov. 2019. [Online]. Available: http://dx.doi.org/10.1038/s41534-019-0199-y

    doi:10.1038/s41534-019-0199-y 2019

  25. [25]

    A fifth-order perturbation comparison of electron correlation theories,

    K. Raghavachari, G. W. Trucks, J. A. Pople, and M. Head-Gordon, “A fifth-order perturbation comparison of electron correlation theories,” Chemical Physics Letters , vol. 157, no. 6, pp. 479–483, 1989

    work page 1989

  26. [26]

    Towards a full ccsdt model for electron correlation,

    M. Urban, J. Noga, S. J. Cole, and R. J. Bartlett, “Towards a full ccsdt model for electron correlation,” The Journal of chemical physics , vol. 83, no. 8, pp. 4041–4046, 1985

    work page 1985

  27. [27]

    Initial state preparation for quantum chemistry on quantum computers,

    S. Fomichev, K. Hejazi, M. S. Zini, M. Kiser, J. Fraxanet, P. A. M. Casares, A. Delgado, J. Huh, A.-C. V oigt, J. E. Mueller et al. , “Initial state preparation for quantum chemistry on quantum computers,” PRX Quantum, vol. 5, no. 4, p. 040339, 2024

    work page 2024

  28. [28]

    Rapid initial state preparation for the quantum simulation of strongly correlated molecules,

    D. W. Berry, Y . Tong, T. Khattar, A. White, T. I. Kim, S. Boixo, L. Lin, S. Lee, G. K. Chan, R. Babbush et al., “Rapid initial state preparation for the quantum simulation of strongly correlated molecules,” arXiv preprint arXiv:2409.11748, 2024

    work page arXiv 2024

  29. [29]

    Faster ground state preparation and high-precision ground energy estimation with fewer qubits,

    Y . Ge, J. Tura, and J. I. Cirac, “Faster ground state preparation and high-precision ground energy estimation with fewer qubits,” Journal of Mathematical Physics , vol. 60, 2019. [Online]. Available: https://doi.org/10.1063/1.5027484

    doi:10.1063/1.5027484 2019

  30. [30]

    Qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,

    D. W. Berry, C. Gidney, M. Motta, J. R. McClean, and R. Babbush, “Qubitization of arbitrary basis quantum chemistry leveraging sparsity and low rank factorization,” Quantum, vol. 3, p. 208, Dec. 2019. [Online]. Available: http://dx.doi.org/10.22331/q-2019-12-02-208

    doi:10.22331/q-2019-12-02-208 2019

  31. [31]

    The complexity of the local hamiltonian problem,

    J. Kempe, A. Kitaev, and O. Regev, “The complexity of the local hamiltonian problem,” 2005. [Online]. Available: https://arxiv.org/abs/ quant-ph/0406180

    Pith/arXiv arXiv 2005

  32. [32]

    Open shell electronic state calculations on quantum computers: A quantum circuit for the preparation of configuration state functions based on serber construction,

    ——, “Open shell electronic state calculations on quantum computers: A quantum circuit for the preparation of configuration state functions based on serber construction,” Chemical Physics Letters , vol. 737, p. 100002, 2019

    work page 2019

  33. [33]

    Postponing the orthogonality catastrophe: efficient state preparation for electronic structure simulations on quantum devices,

    N. M. Tubman, C. Mejuto-Zaera, J. M. Epstein, D. Hait, D. S. Levine, W. Huggins, Z. Jiang, J. R. McClean, R. Babbush, M. Head-Gordon, and K. B. Whaley, “Postponing the orthogonality catastrophe: efficient state preparation for electronic structure simulations on quantum devices,” 2018. [Online]. Available: https://arxiv.org/abs/1809.05523

    Pith/arXiv arXiv 2018

  34. [34]

    Preparation of matrix product states with log-depth quantum circuits,

    D. Malz, G. Styliaris, Z.-Y . Wei, and J. I. Cirac, “Preparation of matrix product states with log-depth quantum circuits,” Physical Review Letters, vol. 132, p. 040404, 2024

    work page 2024

  35. [35]

    Chemical basis of trotter-suzuki errors in quantum chemistry simulation,

    R. Babbush, J. McClean, D. Wecker, A. Aspuru-Guzik, and N. Wiebe, “Chemical basis of trotter-suzuki errors in quantum chemistry simulation,” Phys. Rev. A , vol. 91, p. 022311, Feb 2015. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevA.91.022311

    doi:10.1103/physreva.91.022311 2015

  36. [36]

    Quantum chemistry on quantum computers: A polynomial-time quantum algorithm for constructing the wave functions of open-shell molecules,

    K. Sugisaki, S. Yamamoto, S. Nakazawa, K. Toyota, K. Sato, D. Sh- iomi, and T. Takui, “Quantum chemistry on quantum computers: A polynomial-time quantum algorithm for constructing the wave functions of open-shell molecules,” The Journal of Physical Chemistry A , vol. 120, pp. 6459–6466, 2016

    work page 2016

  37. [37]

    Variational benchmarks for quantum many-body problems,

    D. Wu, R. Rossi, F. Vicentini, N. Astrakhantsev, F. Becca, X. Cao, J. Carrasquilla, F. Ferrari, A. Georges, M. Hibat-Allah et al., “Variational benchmarks for quantum many-body problems,” Science, vol. 386, no. 6719, pp. 296–301, 2024

    work page 2024

  38. [38]

    Barren plateaus in quantum neural network training landscapes,

    J. R. McClean, S. Boixo, V . N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,”Nature communications, vol. 9, no. 1, p. 4812, 2018

    2018

  39. [39]

    Practicality of a quantum adiabatic algorithm for chemistry applications,

    E. Granet, K. Ghanem, and H. Dreyer, “Practicality of a quantum adiabatic algorithm for chemistry applications,” Physical Review A , vol. 111, no. 2, Feb. 2025. [Online]. Available: http://dx.doi.org/10.1103/ PhysRevA.111.022428

    work page 2025

  40. [40]

    Simulated quantum computation of molecular energies,

    A. Aspuru-Guzik, A. D. Dutoi, P. J. Love, and M. HeadGordon, “Simulated quantum computation of molecular energies,” Science, vol. 309, p. 1704, 2005

    work page 2005

  41. [41]

    Exponentially more precise quantum simulation of fermions in second quantization,

    R. Babbush, D. W. Berry, I. D. Kivlichan, A. Y . Wei, P. J. Love, and A. Aspuru-Guzik, “Exponentially more precise quantum simulation of fermions in second quantization,” New Journal of Physics , vol. 18, no. 3, p. 033032, 2016

    work page 2016

  42. [42]

    Exact and efficient lanczos method on a quantum computer,

    W. Kirby, M. Motta, and A. Mezzacapo, “Exact and efficient lanczos method on a quantum computer,” Quantum, vol. 7, p. 1018, 2023

    work page 2023

  43. [43]

    Quantum economic calculator,

    Massachusetts Institute of Technology; Computer Science and Arti- ficial Intelligence Lab and the Initiative on the Digital Economy, “Quantum economic calculator,” https://futuretech.mit.edu/quantum- economic-advantage-calculator

    work page

  44. [44]

    Introducing the quantum economic advantage online calculator,

    F. Mejia, H. Gundlach, J. Lynch, C. Dukatz, A. Lucas, E. Crane, P. Shukla, and N. Thompson, “Introducing the quantum economic advantage online calculator,” Working paper, 2025

    work page 2025

  45. [45]

    Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution,

    M. Motta, C. Sun, A. T. Tan, M. J. O’Rourke, E. Ye, A. J. Minnich, F. G. Brandao, and G. K.-L. Chan, “Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution,” Nature Physics, vol. 16, no. 2, pp. 205–210, 2020

    2020

  46. [46]

    Quantum filter diagonalization: Quantum eigendecomposition without full quantum phase estimation,

    R. M. Parrish and P. L. McMahon, “Quantum filter diagonalization: Quantum eigendecomposition without full quantum phase estimation,” arXiv preprint arXiv:1909.08925 , 2019

    Pith/arXiv arXiv 1909

  47. [47]

    Gschwendtner, N

    M. Gschwendtner, N. Mohr, N. Morgan, and H. Soller. (2023, Dec.) Potential and challenges of quantum computing hardware technologies. Accessed: 2025-04-12. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our- insights/tech-forward/potential-and-challenges-of-quantum-computing- hardware-technologies

    work page 2023

  48. [48]

    42 years of microprocessor trend data,

    K. Rupp, “42 years of microprocessor trend data,” https://www. karlrupp.net/2018/02/42-years-of-microprocessor-trend-data/, 2018, ac- cessed: 2025-04-12

    work page 2018

  49. [49]

    Can quantum computing and deep learning become entangled,

    H. Gundlach, H. Kukina, J. Lynch, and N. Thompson, “Can quantum computing and deep learning become entangled,” MIT CSAIL, Tech. Rep. Working Paper 2025-04, 2025

    work page 2025

  50. [50]

    Focus beyond quadratic speedups for error-corrected quantum advantage,

    R. Babbush, J. R. McClean, M. Newman, C. Gidney, S. Boixo, and H. Neven, “Focus beyond quadratic speedups for error-corrected quantum advantage,” PRX Quantum, vol. 2, no. 1, Mar. 2021. [Online]. Available: http://dx.doi.org/10.1103/PRXQuantum.2.010103

    doi:10.1103/prxquantum.2.010103 2021

  51. [51]

    Forecasting timelines of quantum computing,

    J. Sevilla and C. J. Riedel, “Forecasting timelines of quantum computing,” 2020. [Online]. Available: https://arxiv.org/abs/2009.05045

    Pith/arXiv arXiv 2020

  52. [52]

    Encoding electronic spectra in quantum circuits with linear t complexity,

    R. Babbush, C. Gidney, D. W. Berry, N. Wiebe, J. McClean, A. Paler, A. Fowler, and H. Neven, “Encoding electronic spectra in quantum circuits with linear t complexity,” Physical Review X , vol. 8, no. 4, p. 041015, 2018

    work page 2018

  53. [53]

    Go green: Selected configuration interaction as a more sustainable alternative for high accuracy,

    P.-F. Loos, Y . Damour, A. Ammar, M. Caffarel, F. Kossoski, and A. Scemama, “Go green: Selected configuration interaction as a more sustainable alternative for high accuracy,” 2024. [Online]. Available: https://arxiv.org/abs/2402.13111

    Pith/arXiv arXiv 2024

  54. [54]

    Quantum simulation of exact electron dynamics can be more efficient than classical mean- field methods,

    R. Babbush, W. J. Huggins, D. W. Berry, S. F. Ung, A. Zhao, D. R. Reichman, H. Neven, A. D. Baczewski, and J. Lee, “Quantum simulation of exact electron dynamics can be more efficient than classical mean- field methods,” Nature Communications , vol. 14, no. 1, Jul. 2023. [Online]. Available: http://dx.doi.org/10.1038/s41467-023-39024-0

    work page doi:10.1038/s41467-023-39024-0 2023

  55. [55]

    (2024) Ibm quantum roadmap 2024

    IBM. (2024) Ibm quantum roadmap 2024. Accessed: 2025-04-12. [Online]. Available: https://www.ibm.com/roadmaps/quantum/2024/

    work page 2024

  56. [56]

    Quantum resources required for binding affinity calculations of amyloid beta,

    M. Otten, T. W. Watts, S. D. Johnson, R. Sundareswara, Z. Wang, T. S. Hardikar, K. Heitritter, J. Brown, K. Setia, and A. Holmes, “Quantum resources required for binding affinity calculations of amyloid beta,” arXiv preprint arXiv:2406.18744 , 2024

    Pith/arXiv arXiv 2024

  57. [57]

    Quantum advantage in learning from experiments,

    H.-Y . Huang, M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill et al., “Quantum advantage in learning from experiments,” Science, vol. 376, no. 6598, pp. 1182– 1186, 2022

    work page 2022

  58. [58]

    Performance of coupled-cluster singles and doubles on modern stream processing architectures,

    S. Yue, D. B. Williams-Young, W. de Jong, A. Asadchev, D. Popovici, D. Clark, J. Waldrop, T. Windus, and E. Valeev, “Performance of coupled-cluster singles and doubles on modern stream processing architectures,” J. Chem. Theory Comput. , vol. 16, no. 7, pp. 4021–4028,

    work page

  59. [59]

    Quantum chemistry (qc) on gpus,

    NVIDIA Corporation, “Quantum chemistry (qc) on gpus,” NVIDIA, Technical Report, May 2016. [Online]. Available: https://images.nvidia. com/content/tesla/pdf/quantum-chemistry-may-2016-mb-slides.pdf

    work page 2016

  60. [60]

    Learning smooth and expressive interatomic potentials for physical property prediction,

    X. Fu, B. M. Wood, L. Barroso-Luque, D. S. Levine, M. Gao, M. Dzamba, and C. L. Zitnick, “Learning smooth and expressive interatomic potentials for physical property prediction,” arXiv preprint arXiv:2502.12147, 2025

    Pith/arXiv arXiv 2025

  61. [61]

    (2024, 11) Why ai could eat quantum computing’s lunch

    MIT Technology Review. (2024, 11) Why ai could eat quantum computing’s lunch. Accessed: March 27, 2025. [Online]. Avail- able: https://www.technologyreview.com/2024/11/07/1106730/why-ai- could-eat-quantum-computings-lunch/

    work page 2024

  62. [62]

    Amazon ec2 capacity blocks for ml pricing,

    A. W. Services, “Amazon ec2 capacity blocks for ml pricing,” https: //aws.amazon.com/ec2/capacityblocks/pricing/, accessed: April 7, 2025

    work page 2025

  63. [63]

    Nvidia h100 tensor core gpu datasheet,

    N. Corporation, “Nvidia h100 tensor core gpu datasheet,” https://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core- gpu-datasheet, accessed: April 7, 2025

    work page 2025

  64. [66]

    Pricing plans for azure quantum providers,

    Microsoft, “Pricing plans for azure quantum providers,” https://learn.microsoft.com/en-us/azure/quantum/pricing?tabs=tabid- paygo%2Ctabid-paygoPasqal%2Ctabid-H2, 2025, accessed: April 7, 2025

    work page 2025

  65. [67]

    Ibm creates significant competitive advantages with qiskit runtime updates

    S. Helsel, “Ibm creates significant competitive advantages with qiskit runtime updates.”

    work page

  66. [2017]

    Available: https://www.pnas.org/doi/abs/10.1073/pnas

    [Online]. Available: https://www.pnas.org/doi/abs/10.1073/pnas. 1619152114

    doi:10.1073/pnas

  67. [2020]

    Available: https://doi.org/10.1021/acs.jctc.0c00336

    [Online]. Available: https://doi.org/10.1021/acs.jctc.0c00336

    work page doi:10.1021/acs.jctc.0c00336

  68. [2023]

    Available: https://arxiv.org/abs/2310.15505

    [Online]. Available: https://arxiv.org/abs/2310.15505

    Pith/arXiv arXiv

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.