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REVIEW 2 major objections 5 minor 1 cited by

Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read Most standard electronic-structure ansätze cannot be efficiently parallelized with local-operations circuit cutting, because their shot overhead scales as 9 to the number of cut controlled-X or controlled-Z gates, while LUCJ stays small.

desk verdict Useful LUCJ pointer and correct overhead math, but the infeasibility verdict overreaches from a single fixed partition. read the letter →

arxiv 2507.01902 v1 pith:YX5DEKXL submitted 2025-07-02 quant-ph cs.DCphysics.chem-ph

classification quant-phcs.DCphysics.chem-ph MSC 81P6881V55 PACS 03.67.Ac03.67.Lx
keywords electronicstructuretheorydistributedquantumcomputingcircuitcuttinglocaloperationssamplingoverheadunitarycoupledclusterLUCJansatzchemistry
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 asks whether five standard quantum-chemistry trial wave functions can be split across multiple small quantum processors by circuit cutting with local operations (LO), the decomposition of two-qubit gates into mixtures of single-qubit operations without classical communication. It reports that four of the five ansätze—UCCSD, UpCCD, UpCCGSD, and SPA+GS—suffer sampling overheads equal to $9$ raised to the number of cut controlled-X or controlled-Z gates, so even the smallest molecule, $\mathrm{H}_2$ in a minimal basis, demands $9^{18} \approx 1.5\times10^{17}$ circuit evaluations for UCCSD with the Jordan-Wigner encoding. The fifth ansatz, the local unitary cluster Jastrow (LUCJ) circuit, cuts small-angle controlled-phase gates and keeps its total overhead at or below $5.5$ for hydrogen chains up to $\mathrm{H}_{50}$. The authors conclude that most current electronic-structure algorithms cannot be parallelized efficiently with LO circuit cutting and that distributed quantum chemistry needs new ansätze or new cutting protocols.

What carries the argument

The load-bearing mechanism is LO circuit cutting viewed through quasiprobability simulation: a two-qubit gate $U$ crossing the partition is replaced by a random choice of single-qubit channels $F_i$ with coefficients $a_i$, and the cost is the squared sampling overhead $\kappa^2 = (\sum_i |a_i|)^2$, with the total overhead for a circuit equal to the product over all cut gates of $\gamma_S(U)^2$. For controlled-X and controlled-Z gates this per-gate factor is fixed at $9$, which turns every additional cut into a factor-of-$9$ multiplier; for controlled-phase gates it is $(1+2|\sin(\theta/2)|)^2$, which is close to $1$ when the angle is small. The paper's argument consists of counting the controlled-X, controlled-Z, and controlled-phase gates that cross the chosen partition in each ansatz and then evaluating these factors for hydrogen-chain systems.

What would settle it

Search all two-way partitions of the qubit register for UCCSD with the Jordan-Wigner encoding on $\mathrm{H}_2$ (and on larger $\mathrm{H}_{2n}$ chains) and recompute the LO sampling overhead from the minimum number of crossing controlled-X and controlled-Z gates; if any partition yields a cut count whose total overhead grows polynomially rather than as $9^{N_{\mathrm{cuts}}}$, the paper's infeasibility conclusion for that ansatz would be overturned.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a systematic count of what happens when each ansatz is cut in half at a fixed partition under LO. For UCCSD, UpCCD, UpCCGSD, and SPA+GS, every two-qubit gate crossing the cut is a controlled-X or controlled-Z gate, and each such gate contributes a sampling-overhead factor of $\gamma^2 = 9$; the total overhead is therefore $9^{N_{\mathrm{cuts}}}$. For $\mathrm{H}_2$ with UCCSD this is already $1.5\times10^{17}$ under the Jordan-Wigner encoding and $7.98\times10^{22}$ under Bravyi-Kitaev, and for every larger hydrogen chain the overhead exceeds the double-precision maximum of $1.7977\times10^{308}$ and is treated as infinite. LUCJ behaves differently because its cut gates are controlled-phase rotations with small angles $\theta$, carrying overhead $(1+2|\sin(\theta/2)|)^2$ each; the maximum total overhead across $\mathrm{H}_2$ through $\mathrm{H}_{50}$ and one to five circuit layers is $5.5$ for all-to-all connectivity and $2.08$ for heavy-hex. The conclusion drawn is that most common ansätze are impractical to distribute with local operations, while LUCJ is a promising exception.

Load-bearing premise

The conclusions rest on cutting each circuit at one fixed partition—the middle of the qubit register, or the alpha/beta boundary for LUCJ—with zero-valued ansatz parameters, so a different partition or parameter-dependent gate layout could change the cut counts and possibly the feasibility verdict.

Editorial extensions

If this is right

  • For UCCSD, UpCCD, UpCCGSD, and SPA+GS, any LO-distributed implementation would need the uncut circuit's shot count multiplied by $9^{N_{\mathrm{cuts}}}$, so for $\mathrm{H}_2$ already the unpartitioned circuit is more efficient.
  • For hydrogen chains larger than $\mathrm{H}_2$, the reported overhead for these four ansätze exceeds $1.7977\times10^{308}$ and is effectively infinite, meaning an LO-distributed run would never finish.
  • LUCJ remains practical under LO cutting: at most about $5.5$ extra shot overhead for $\mathrm{H}_{50}$ with all-to-all connectivity and $2.08$ with heavy-hex, across one to five layers.
  • Fermionic encoding choice (Jordan-Wigner vs Bravyi-Kitaev) changes the number of cuts but does not change the qualitative verdict for the CX/CZ-based ansätze.
  • New distributed electronic-structure methods, such as distributed unitary selective coupled cluster, are needed if distributed quantum computing is to serve quantum chemistry.

Reading between the lines

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

  • The analysis fixes a single partition per circuit; searching over all two-way partitions could reduce the cut count for some ansätze, though it is not obvious it would change the exponential character for CX/CZ-dominated circuits.
  • Because the overhead is set by the type of cut gate, not by the chemistry, the same verdict would likely carry over to larger molecules and basis sets whenever a middle-register cut crosses many CX/CZ gates.
  • A testable extension is to allow one-way classical communication ($\mathrm{LO}{\rightarrow}\mathrm{CC}$) or LOCC, whose per-gate overheads are no larger than LO; the paper does not quantify those reductions, so the feasibility boundary for these ansätze under those protocols remains open.
  • For LUCJ, the small overhead suggests a concrete design rule for future distributed chemistry circuits: keep cut two-qubit gates as small-angle phase rotations and minimize their number; the paper's data support this rule but do not state it as a general principle.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 5 minor

Summary. The paper analyzes the feasibility of executing five electronic structure ansätze (UCCSD, UpCCD, UpCCGSD, SPA+GS, and LUCJ) on distributed quantum computers using circuit cutting with local operations (LO). Circuits are generated with Tequila and ffsim, converted to Qiskit, and cut along a single fixed bipartition. The authors count the number of crossing two-qubit gates and compute LO sampling overheads using known decompositions (9^Ncuts for CX/CZ gates, (1+2|sin(theta/2)|)^2 per cut CP gate). They report that UCCSD, UpCCD, UpCCGSD, and SPA+GS have enormous overheads even for H2, while LUCJ has modest overheads that scale gently with system size, and conclude that most of these ansätze cannot be efficiently parallelized with LO and that new distributed electronic structure methods are needed.

Significance. If the reported overheads were representative of the ansätze in general, the paper would provide a useful negative benchmark for distributed quantum chemistry and a concrete motivation for designing new ansätze that are more amenable to circuit cutting. The work is transparent: the overhead arithmetic is straightforward, the external decompositions are referenced, and the authors provide a GitHub repository with the data. The favorable scaling of LUCJ is an interesting and falsifiable finding. However, the strength of the central claim is currently limited by the absence of any exploration over circuit partitions, qubit orderings, and parameter values; the paper mostly demonstrates that one particular compilation of each ansatz is expensive to cut, not that the ansatz itself is inherently incompressible under LO.

major comments (2)
  1. [Section IV, fixed bipartition] Every reported Ncuts is computed for a single fixed bipartition: the Tequila circuits are 'cut in half, between qubits q1 and q2' and the LUCJ circuits are partitioned at the alpha/beta boundary. Since Ncuts is the exponent in the sampling overhead 9^Ncuts, the overhead numbers cited in the paper are not properties of the ansatz alone but of one chosen partition and orbital ordering. A different ordering can move two-qubit gates entirely into one partition, reducing Ncuts dramatically; for example, reducing the H2 UCCSD/JW count from 18 cuts to 6 cuts would lower the overhead from about 1.5e17 to about 5.3e5. Thus the conclusion that these ansätze 'cannot be efficiently parallelized using LO' is currently established only for the specific partition and compilation tested. I request that the authors either perform a scan over partitions and qubit/orbital orderings for at least H2 and H10, or explicitly restrict the conclusion to the tested configuration rather than to the algorithms in general.
  2. [Section III, zero-parameter initialization] The paper states that initializing all Tequila parameters to zero does not affect the sampling overhead analysis because the non-local gate cuts do not correspond to parameterized gates. This assumption is not verified. With all parameters set to zero, parameterized rotations become identities and entire excitation operators may simplify away or cancel adjacent two-qubit gates during compilation, which would reduce the number of crossing CX/CZ gates; conversely, nonzero amplitudes could introduce additional two-qubit structure. The authors should compare cut counts for at least one nonzero parameter set (for example MP2 or random amplitudes) and explicitly report the Qiskit optimization level used, so that the reader can see that Ncuts is stable under parameter choices.
minor comments (5)
  1. [Section IV, H2 UpCCD/JW overhead] The text reports a 'sampling overhead of 10^4 = 6.5610×10^3' for the UpCCD/JW circuit; the correct expression is 9^4 = 6.5610×10^3, and the typo should be fixed.
  2. [References [25] and [39]] Reference [25] is misattributed: the UpCCD and UpCCGSD methods are introduced in Y. Lee et al., 'Generalized unitary coupled cluster wave functions for quantum computation,' J. Chem. Theory Comput. 15, 311 (2019), which is listed as reference [39], not in the npj Quantum Information article cited as [25].
  3. [Section IV and Fig. 3, 'infinite' overhead] Labeling every sampling overhead above the NumPy double-precision maximum (1.7977e308) as 'infinite' conflates numerical overflow with a mathematical divergence. I suggest reporting log10 overheads or saying 'exceeds the double-precision range' instead, since the qualitative point is already made by the enormous finite values.
  4. [Fig. 3] The figure is difficult to read as printed: the legend and line styles for the different H2n chain lengths are not differentiated in the caption, and it is not stated whether the vertical axes are log-scaled. Please add a clear caption and, if possible, use distinguishable markers or separate panels.
  5. [Conclusion] The concluding statement that 'new methods must be developed' for distributed electronic structure should consistently be qualified as applying to the LO setting, since Eq. (13) notes that LOCC can reduce sampling overhead and the paper only analyzes LO because of the current Qiskit addon implementation.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's overhead numbers are direct applications of external circuit-cutting formulas to fixed circuit compilations; the central claim is an empirical assessment, not a derivation that reduces to its inputs.

full rationale

The paper's central claim is an empirical assessment of the sampling overhead required to cut specific electronic-structure circuits using local operations. The overhead formulas, 9^Ncuts for CX/CZ gates and ((1+2|sin(theta/2)|)^2)^Ncuts for CP gates, are imported directly from external literature [43,44], and the gate-cut counts are obtained by inspecting circuits generated by Tequila and ffsim. No parameter is fitted, no target result is assumed in the construction, and no conclusion is derived from the authors' own prior work. The only self-citation, [19], supports the background statement that examples of DQC within chemistry are lacking; it is not load-bearing for any numerical result. Several limitations exist, such as the fixed single partition between qubits q1 and q2, the LO-only setting, and the zero-initialization of Tequila parameters with an unverified claim that parameter values do not affect gate cuts. These are validity and scope concerns that could weaken the generality of the conclusion, but they do not make the output equivalent to the input by construction. Accordingly, no circular step is present and the circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper's overhead calculations rely on standard circuit-cutting formulas from external references and on two unverified modeling choices: the fixed partition and zero-parameter circuits. No free parameters are fitted, and no new entities are introduced.

assumptions (4)
  • domain assumption Total sampling overhead for multiple cuts is the product of per-gate overheads, prod gamma(U_i)^2
    Standard quasiprobability circuit-cutting result; the paper applies it without derivation, citing [28,43,44].
  • domain assumption For LO, gamma(CX)=gamma(CZ)=3 and gamma(CP(theta))=1+2|sin(theta/2)|
    Values are taken from [43,44] and used directly in Section IV.
  • ad hoc to paper The fixed middle-qubit bipartition is representative for assessing distributability
    The paper cuts each circuit at one predetermined boundary and does not search over alternative partitions; see Section IV.
  • ad hoc to paper Zero-parameter initialized Tequila circuits yield gate structures representative of the ansatz
    The paper initializes UCCSD/UpCCD/UpCCGSD/SPA+GS parameters to zero and asserts this does not affect the cut-gate analysis; see Section III.

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Cite this review

Pith. "Pith review of Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing." pith.science (2026). https://pith.science/paper/YX5DEKXL

@misc{pith2026250701902,
  author       = {Pith},
  title        = {Pith review of: Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YX5DEKXL}},
  note         = {Machine review of arXiv:2507.01902}
}
read the original abstract

To move towards the utility era of quantum computing, many corporations have posed distributed quantum computing (DQC) as a framework for scaling the current generation of devices for practical applications. One of these applications is quantum chemistry, also known as electronic structure theory, which has been poised as a "killer application" of quantum computing, To this end, we analyze five electronic structure methods, found in common packages such as Tequila and ffsim, which can be easily interfaced with the Qiskit Circuit Cutting addon. Herein, we provide insights into cutting these algorithms using local operations (LO) to determine their aptitude for distribution. The key findings of our work are that many of these algorithms cannot be efficiently parallelized using LO, and new methods must be developed to apply electronic structure theory within a DQC framework.

Figures

Figures reproduced from arXiv: 2507.01902 by the authors.

Figure 1
Figure 1. An example of spatial-orbitals (left), denoted as [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example quantum circuits for H2 using a minimal basis, STO-3G, with an active space of two electrons in two orbitals, (2,2). The circuits we explore are as follows: (a) UCCSD/Jordan￾Wigner, (b) UCCSD/Bravyi-Kitaev, (c) UpCCD/Jordan-Wigner, (d) UpCCD/Bravyi-Kitaev, (e) UpCCGSD/Jordan-Wigner, (f) UpCCGSD/Bravyi-Kitaev, (g) SPA+GS/Jordan-Wigner, (h) SPA+GS/Bravyi-Kitaev, and (i) LUCJ using an all-to-all and (j) heavy-h… view at source ↗
Figure 3
Figure 3. (a) The sampling overhead (top row) and number of [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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Reference graph

Works this paper leans on

47 extracted references · 45 canonical work pages · cited by 1 Pith paper

  1. [25]

    A quantum router architecture for high-fidelity entanglement flows in quantum networks,

    Y . Lee et al. , “A quantum router architecture for high-fidelity entanglement flows in quantum networks,” npj Quantum Inf , vol. 8, no. 1, pp. 1–8, Jun. 2022

  2. [39]

    Generalized unitary coupled cluster wave functions for quantum computation,

    J. Lee et al. , “Generalized unitary coupled cluster wave functions for quantum computation,” Journal of chemical theory and computation, vol. 15, no. 1, pp. 311–324, 2018

  3. [1]

    Simulated quantum computation of molec- ular energies,

    A. Aspuru-Guzik et al., “Simulated quantum computation of molec- ular energies,” Science, vol. 309, no. 5741, pp. 1704–1707, 2005. (a) (b) Fig. 3: (a) The sampling overhead (top row) and number of cuts (bottom row) for various methods implemented in Tequila using both the Jordan-Wigner and Bravyi-Kitaev Fermionic encodings. (b) The sampling overhead (top ...

  4. [2]

    Quantum Chemistry in the Age of Quantum Computing,

    Y . Cao et al. , “Quantum Chemistry in the Age of Quantum Computing,” Chemical Reviews , vol. 119, no. 19, pp. 10 856–10 915, Oct. 2019

  5. [3]

    Quantum computational chemistry,

    S. McArdle et al. , “Quantum computational chemistry,” Reviews of Modern Physics , vol. 92, no. 1, Mar. 2020

  6. [4]

    Simulation of many-body Fermi systems on a universal quantum computer,

    D. S. Abrams et al. , “Simulation of many-body Fermi systems on a universal quantum computer,” Physical Review Letters , vol. 79, no. 13, p. 2586, 1997

  7. [5]

    Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors,

    ——, “Quantum algorithm providing exponential speed increase for finding eigenvalues and eigenvectors,” Physical Review Letters , vol. 83, no. 24, p. 5162, 1999

  8. [6]

    Towards quantum chemistry on a quantum computer,

    B. P. Lanyon et al. , “Towards quantum chemistry on a quantum computer,” Nature chemistry, vol. 2, no. 2, pp. 106–111, 2010

Show all 47 references
  1. [7]

    Simulation of electronic structure Hamiltonians using quantum computers,

    J. D. Whitfield et al. , “Simulation of electronic structure Hamiltonians using quantum computers,” Molecular Physics , vol. 109, no. 5, pp. 735–750, 2011

  2. [8]

    Photonic quantum simulators,

    A. Aspuru-Guzik et al. , “Photonic quantum simulators,” Nature physics, vol. 8, no. 4, pp. 285–291, 2012

  3. [9]

    A variational eigenvalue solver on a photonic quantum processor,

    A. Peruzzo et al. , “A variational eigenvalue solver on a photonic quantum processor,” Nature Communications , vol. 5, no. 1, p. 4213, Jul. 2014

  4. [10]

    Variational quantum algorithms,

    M. Cerezo et al. , “Variational quantum algorithms,” Nature Reviews Physics, vol. 3, no. 9, pp. 625–644, Sep. 2021

  5. [11]

    The theory of variational hybrid quantum-classical algorithms,

    J. R. McClean et al. , “The theory of variational hybrid quantum-classical algorithms,” New Journal of Physics , vol. 18, no. 2, p. 023023, Feb. 2016

  6. [12]

    Noisy intermediate-scale quantum algorithms,

    K. Bharti et al. , “Noisy intermediate-scale quantum algorithms,” Reviews of Modern Physics , vol. 94, no. 1, p. 015004, Feb. 2022

  7. [13]

    Variational quantum eigensolvers in the era of distributed quantum computers,

    I. Khait et al. , “Variational quantum eigensolvers in the era of distributed quantum computers,” Physical Review A , vol. 108, no. 5, p. L050401, 2023

  8. [14]

    Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing,

    D. Barral et al. , “Review of Distributed Quantum Computing. From single QPU to High Performance Quantum Computing,” Apr. 2024. [Online]. Available: http://arxiv.org/abs/2404.01265

  9. [15]

    Distributed quantum computing: a new frontier in distributed systems or science fiction?

    V . S. Denchev et al. , “Distributed quantum computing: a new frontier in distributed systems or science fiction?” SIGACT News, vol. 39, no. 3, pp. 77–95, Sep. 2008

  10. [16]

    Parallel implementation of electronic structure energy, gradient, and Hessian calculations,

    V . Lotrichet al. , “Parallel implementation of electronic structure energy, gradient, and Hessian calculations,” The Journal of Chemical Physics , vol. 128, no. 19, p. 194104, May 2008

  11. [17]

    Chapter 2 - Quantum Chemistry on Graphics Processing Units,

    A. W. G ¨otz et al. , “Chapter 2 - Quantum Chemistry on Graphics Processing Units,” in Annual Reports in Computational Chemistry, R. A. Wheeler, Ed., vol. 6, pp. 21–35

  12. [18]

    Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing,

    U. Raucci et al. , “Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing,” Annual Review of Physical Chemistry , vol. 74, no. V olume 74, 2023, pp. 313–336, Apr. 2023

  13. [19]

    Distributed quantum computing for chemical applications,

    G. M. Jones et al. , “Distributed quantum computing for chemical applications,” in 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) , vol. 2, pp. 155–160

  14. [20]

    Elucidating reaction mechanisms on quantum computers,

    M. Reiher et al. , “Elucidating reaction mechanisms on quantum computers,” Proceedings of the national academy of sciences , vol. 114, no. 29, pp. 7555–7560, 2017

  15. [21]

    Error analysis and improvements of coupled- cluster theory,

    W. Kutzelnigg, “Error analysis and improvements of coupled- cluster theory,” Theoretica chimica acta , vol. 80, pp. 349–386, 1991

  16. [22]

    Alternative coupled-cluster ans ¨atze ii. the unitary coupled-cluster method,

    R. J. Bartlett et al. , “Alternative coupled-cluster ans ¨atze ii. the unitary coupled-cluster method,” Chemical physics letters , vol. 155, no. 1, pp. 133–140, 1989

  17. [23]

    A unitary multiconfigurational coupled- cluster method: Theory and applications,

    M. R. Hoffmann et al. , “A unitary multiconfigurational coupled- cluster method: Theory and applications,” The Journal of chemical physics, vol. 88, no. 2, pp. 993–1002, 1988

  18. [24]

    The expectation value coupled-cluster method and analytical energy derivatives,

    R. J. Bartlett et al. , “The expectation value coupled-cluster method and analytical energy derivatives,” Chemical physics letters, vol. 150, no. 1-2, pp. 29–36, 1988

  19. [26]

    Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approx- imation,

    J. S. Kottmann et al. , “Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approx- imation,” Physical Review A , vol. 105, no. 3, p. 032449, 2022

  20. [27]

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

    M. Motta et al. , “Bridging physical intuition and hardware efficiency for correlated electronic states: the local unitary cluster jastrow ansatz for electronic structure,” Chemical Science , vol. 14, no. 40, pp. 11 213–11 227, 2023

  21. [28]

    Circuit knitting with classical communication,

    C. Piveteau et al. , “Circuit knitting with classical communication,” IEEE Transactions on Information Theory , vol. 70, no. 4, pp. 2734–2745, 2023

  22. [29]

    Tequila: A platform for rapid development of quantum algorithms,

    J. S. Kottmann et al. , “Tequila: A platform for rapid development of quantum algorithms,” Quantum Science and T echnology, vol. 6, no. 2, p. 024009, 2021

  23. [30]

    ffsim: Faster simulations of fermionic quantum circuits

    The ffsim developers, “ffsim: Faster simulations of fermionic quantum circuits.” [Online]. Available: https://github.com/qiskit-community/ffsim

  24. [31]

    Quantum computing with Qiskit,

    A. Javadi-Abhari et al. , “Quantum computing with Qiskit,” 2024

  25. [32]

    Qiskit addon: circuit cutting,

    A. M. Bra ´nczyk et al. , “Qiskit addon: circuit cutting,” https://github.com/Qiskit/qiskit-addon-cutting, 2024

  26. [33]

    Helgaker et al

    T. Helgaker et al. , Molecular electronic-structure theory . John Wiley & Sons, 2013

  27. [34]

    Coupled-cluster theory in quantum chemistry,

    R. J. Bartlett et al., “Coupled-cluster theory in quantum chemistry,” Reviews of Modern Physics , vol. 79, no. 1, pp. 291–352, 2007

  28. [35]

    Coupled cluster approach,

    J. Cizek et al. , “Coupled cluster approach,” Physica Scripta , vol. 21, no. 3-4, p. 251, 1980

  29. [36]

    On the correlation problem in atomic and molecular systems. calculation of wavefunction components in ursell-type expansion using quantum-field theoretical methods,

    J. ˇC´ıˇzek, “On the correlation problem in atomic and molecular systems. calculation of wavefunction components in ursell-type expansion using quantum-field theoretical methods,” The Journal of Chemical Physics , vol. 45, no. 11, pp. 4256–4266, 1966

  30. [37]

    Post-hartree-fock methods: configuration interaction, many-body perturbation theory, coupled-cluster theory,

    J. Townsend et al. , “Post-hartree-fock methods: configuration interaction, many-body perturbation theory, coupled-cluster theory,” in Mathematical Physics in Theoretical Chemistry , pp. 63–117

  31. [38]

    A quantum computing view on unitary coupled cluster theory,

    A. Anand et al. , “A quantum computing view on unitary coupled cluster theory,” Chemical Society Reviews , vol. 51, no. 5, pp. 1659–1684, 2022

  32. [40]

    Brueckner based generalized coupled cluster theory: Implicit inclusion of higher excitation effects,

    M. Nooijen et al. , “Brueckner based generalized coupled cluster theory: Implicit inclusion of higher excitation effects,” The Journal of Chemical Physics , vol. 113, no. 11, pp. 4549–4557, 2000

  33. [41]

    ¨uber das paulische ¨aquivalenzverbot. z phys 47: 631,

    P. Jordan et al. , “ ¨uber das paulische ¨aquivalenzverbot. z phys 47: 631,” 1928

  34. [42]

    Fermionic quantum computation,

    S. B. Bravyi et al. , “Fermionic quantum computation,” Annals of Physics , vol. 298, no. 1, pp. 210–226, 2002

  35. [43]

    Constructing a virtual two-qubit gate by sampling single-qubit operations,

    K. Mitarai et al. , “Constructing a virtual two-qubit gate by sampling single-qubit operations,” New Journal of Physics , vol. 23, no. 2, p. 023021, 2021

  36. [44]

    Cutting circuits with multiple two-qubit unitaries,

    L. Schmitt et al. , “Cutting circuits with multiple two-qubit unitaries,” Quantum, vol. 9, p. 1634, 2025

  37. [45]

    Array programming with NumPy,

    C. R. Harris et al. , “Array programming with NumPy,” Nature, vol. 585, no. 7825, pp. 357–362, Sep. 2020

  38. [46]

    Efficient algorithms for quantum chemistry on mod- ular quantum processors,

    T. Xue et al., “Efficient algorithms for quantum chemistry on mod- ular quantum processors,” arXiv preprint arXiv:2506.13332 , 2025

  39. [47]

    Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer,

    J. Robledo-Moreno et al. , “Chemistry Beyond Exact Solutions on a Quantum-Centric Supercomputer,” May 2024. [Online]. Available: http://arxiv.org/abs/2405.05068

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