Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Quantum Assisted Ghost Gutzwiller Ansatz

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Sampling a quantum trial state on a 24-qubit device builds a selected configuration-interaction basis that reproduces the Fermi-Hubbard metal-to-insulator transition inside a converged ghost-Gutzwiller self-consistent loop, using as…

desk verdict A genuine hybrid-embedding milestone, but the cut-circuit sampling step needs validation before the 1%-CI claim is fully established. read the letter →

arxiv 2506.21431 v1 pith:MHE63QZK submitted 2025-06-26 quant-ph cond-mat.str-el

P.V. Sriluckshmy , François Jamet , Fedor Šimkovic IV This is my paper
classification quant-phcond-mat.str-el MSC 81P6881V7082B20 PACS 03.67.Ac71.10.Fd71.30.+h
keywords ghostGutzwilleransatzquantum-selectedconfigurationinteractionFermi-HubbardmodelBethelatticemetal-insulatortransitionLUCJcircuitcuttingquantumhardwareembedding
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

The paper establishes that the computational bottleneck of the ghost Gutzwiller ansatz (gGut) — solving the embedding Hamiltonian's ground state inside a self-consistent loop — can be delegated to a hybrid quantum-classical solver without losing the method's qualitative physics. Using quantum-selected configuration interaction (QSCI), the authors sample basis states from a local unitary cluster Jastrow (LUCJ) trial state prepared on a 20-qubit superconducting processor, with wire cutting to separate spin sectors, and diagonalize the embedding Hamiltonian in the sampled subspace. They show that for the single-band Fermi-Hubbard model on the Bethe lattice the relevant ground states become sparser as the number of ghost orbitals grows, and that a truncation to about 1% of the full configuration-interaction basis still reproduces the metal-to-insulator transition in the density of states up to Ng = 11 ghost orbitals (24 qubits). The result matters because it demonstrates a working, hardware-executed quantum impurity solver for an embedding method that otherwise scales exponentially.

What carries the argument

The load-bearing mechanism is the combination of (i) a single-particle basis rotation of the embedding Hamiltonian to the 'star configuration', in which ghost orbitals couple only to the impurity and not to each other, which sharply accelerates the decay of CI coefficients without introducing the O($N^{4}$) two-electron terms of the canonical basis; (ii) quantum-selected configuration interaction (QSCI), which builds the subspace by sampling computational basis states from a trial state instead of selecting them by classical heuristics; (iii) the local unitary cluster Jastrow (LUCJ) ansatz, parameterized by nearest-neighbor density-density interactions and orbital rotations warm-started from CCSD amplitudes, as the trial state; and (iv) Pauli wire cutting, which decomposes the circuit across an impurity-site interaction to separate spin-up and spin-down halves and thereby reduces hardware noise at an O(4^k) sampling overhead. The self-consistent gGut loop then alternates between a quadratic quasi-particle Hamiltonian and the embedding Hamiltonian, with the 1-RDMs rho_emb and zeta computed from the QSCI ground state feeding back into the renormalization matrix $\Omega$.

What would settle it

For Ng = 11 at U = 2, compute the converged 1-RDMs rho_emb and zeta from the p = 1% QSCI ground state and compare them entry-wise against the FCI values; if the error stays large while the low-frequency DOS nevertheless looks converged, the loop's apparent stability is not evidence that the sampled basis captures the configurations that control the density matrices.

Watch

Extended reading notes

Core claim

The paper's central claim is that sample-based quantum-selected configuration interaction can serve as a practical impurity solver inside the ghost Gutzwiller self-consistent loop, on real quantum hardware and at system sizes beyond what exact diagonalization can reach in practice. Concretely, the authors show that for embedding Hamiltonians generated by converged gGut calculations on the Bethe-lattice Hubbard model, the FCI ground state is sparse in the CI basis — increasingly so as the number of ghost orbitals Ng grows and as the interaction U increases — provided the Hamiltonian is first rotated to the star configuration. Building the SCI basis instead from measurements of a classically pre-optimized LUCJ trial state, including runs executed on a 20-qubit device with two wire cuts, yields density-of-states curves that correctly show the quasi-particle peak at weak coupling and the opening of the Mott gap at strong coupling. The decisive quantitative result is that for Ng = 11 (24 qubits) a truncation at 1% of the total CI basis states is enough to capture the gap, and that eleven iterations of the full self-consistent loop remain stable when the 1-RDMs are computed from the truncated SCI ground state.

Load-bearing premise

The method only works if measurements of a cheap approximate quantum state, even after hardware noise and circuit cutting, still pick out the electron configurations that determine the low-frequency response, and if discarding 99% of the remaining configurations leaves the self-consistent loop's density matrices accurate enough.

Editorial extensions

If this is right

  • Because ground-state sparsity grows with Ng and U, selected CI solvers extend the reach of gGut beyond exact diagonalization: extrapolating the measured CI-count growth suggests system sizes of 84–108 qubits (Ng = 41–53) become feasible at the CI budgets of current large-scale FCI implementations.
  • The star configuration is the recommended basis for QSCI within gGut: it gives nearly the CI-weight decay of the canonical basis while avoiding the O(N^4) two-particle Hamiltonian overhead.
  • LUCJ trial states without the SCI step are not sufficient — even noiseless three-layer optimizations produce DOS curves with spurious high-frequency peaks and fail to open the gap at larger Ng — so the configuration-selection step, not the ansatz alone, is what carries the result.
  • Circuit cutting improved the QSCI results compared with uncut hardware runs, despite its sampling overhead, because the smaller sub-circuits are less noisy and the reassembly mixes CI fragments.
  • The full self-consistent gGut loop converges stably for Ng = 11 when the 1-RDMs are computed from a 1%-truncated QSCI ground state, with the low-frequency DOS and the U = 3 gap reproduced across eleven iterations.

Reading between the lines

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

  • A testable extension of the paper's approach is to replace the LUCJ trial state with real- or imaginary-time-evolved states, which are harder to simulate classically and could push the sampled-basis workflow beyond 24 qubits.
  • The sparsity findings likely do not transfer unchanged to other embedding frameworks: the paper's own comparison shows ad hoc Anderson models have very different CI-weight decay than gGut-generated Hamiltonians, so the 1% figure is a property of gGut, not of sampling-based diagonalization in general.
  • The circuit-cutting improvement hints that shallow, decomposable trial circuits could be designed deliberately for sampling-based solvers, treating the cut structure as a feature that reduces noise and reshapes the sampled basis.
Share X Bluesky LinkedIn Reddit HN

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. The manuscript develops a hybrid quantum-classical impurity solver for the ghost Gutzwiller ansatz (gGut) embedding method, applied to the single-band Fermi-Hubbard model on the Bethe lattice. Classically, it studies selected configuration interaction (SCI) truncations of the embedding Hamiltonian's FCI ground state, the effect of single-particle basis rotations (chain, star, canonical), and the scaling of the required CI fraction with ghost number Ng. It then uses LUCJ trial states on IQM's Garnet hardware with Pauli wire cutting to generate CI bases for QSCI, and runs self-consistent gGut loops for Ng=11 (24 qubits) with a truncation of p=0.01. The central claim is that QSCI with circuit cutting reproduces the metal-to-insulator transition in the DOS using as little as 1% of the total CI basis states.

Significance. If validated, the paper would be a significant early demonstration of a closed hybrid quantum-classical embedding loop on real hardware, and its sparsity analysis for gGut embedding Hamiltonians is a useful contribution for future impurity-solver design. The authors are also commendably explicit about limitations: LUCJ alone gives poor DOS (Fig. 5 and App. E), uncut hardware QSCI is far from converged even at p=0.3 (Fig. 6), high-frequency sidebands are not captured at p=0.01 (Figs. 7-8), and gGut convergence at Ng=11 is difficult. However, the load-bearing circuit-cutting sample reconstruction is not specified or validated, so the mechanism behind the hardware results is currently unresolved.

major comments (3)
  1. [Sec. IIIC, Eq. (24), Figs. 7-8] The paper does not specify how computational-basis CI strings are obtained from Pauli wire cutting. Equation (24) expresses the cut qubit state as a signed quasiprobability combination, and reconstructing expectation values requires sampling over the preparation/measurement bases O_i. A simple tensor product of raw partial bitstrings from the two spin sub-circuits is not equivalent to sampling the original LUCJ state, especially because the cut qubits are measured in X/Y/Z bases and their outcomes do not directly give computational-basis occupancies. The paper neither describes how the signs c_i and basis choices are folded into the CI counts nor validates the reassembled distribution against an exact uncut simulation of the same LUCJ state. Since Fig. 5 shows LUCJ alone is a poor DOS approximation and the improvement in Figs. 7-8 is attributed to QSCI selecting relevant CIs from the LUCJ state, a cut-biased distribution would undermine the stated mechanism; the authors' own conclusion that the improvement comes partly from 'mixing of CIs from partial circuits' supports this concern. Please provide a validation comparing cut-reassembled CI weights and distributions to exact uncut LUCJ samples, and specify the weighting scheme.
  2. [Sec. IVB, Fig. 8] The claim of 'converged gGut calculations' is not fully supported by the data shown. The gGut convergence criteria defined in Sec. II are |Omega|, |Lambda_qp|, and Tr(rho_qp) - Tr(rho_emb), but Fig. 8 plots only the DOS over 11 iterations, with no convergence metrics for these quantities or for the 1-RDMs rho_emb and zeta used in step 4. Moreover, Fig. 8 shows that p=0.01 captures the low-frequency DOS and the gap but not the high-frequency sidebands, so it remains possible that the 1-RDMs are not converged at this truncation. Please report the loop convergence measures and, ideally, the p-dependence of rho_emb and zeta.
  3. [Fig. 4d and Sec. IVA] The extrapolation to Ng=41 and Ng=53 relies on an exponential fit a + b exp(cN) to data up to Ng=11, with the authors noting a deviation at Ng=11 and that the data are not fully converged. The resulting statement in the Conclusions that SCI may 'potentially facilitate calculations for systems with more than 100 fermionic modes' is therefore not quantitatively supported. Please soften the claim or provide additional data or uncertainty estimates.
minor comments (5)
  1. [Sec. IVB] There are several typographical errors: 'for for N = 20 qubits' in the hardware circuit discussion, 'Results are shows as a function' in the Fig. 7 caption, 'the same3.2· 105 samples' with a missing space, and 'decreases at a the same pace' in Appendix A.
  2. [Sec. IIIC] The statement that the Pauli circuit-cutting overhead 'scales as O(8^k)' and then 'can be reduced to O(4^k)' is unclear about whether the overhead counts samples, circuit executions, or classical post-processing cost; please define the resource measure explicitly.
  3. [Appendix B] The appendix table lists |S_sym| values, but the main text sometimes uses |S_sym| and |S_SCI| without a single consolidated definition; please define both symbols in one place and use them consistently.
  4. [Sec. IVB and Conclusions] The LUCJ parameter optimization is performed classically with SPSA on a noiseless simulator even for the hardware runs; the abstract's 'using quantum samples' should be qualified early in the main text, even though the Conclusions already acknowledge this.
  5. [Fig. 4c] The figure axis for the rescaled CI index p_x = S_x/|S_sym(Ng)| should be labeled in the figure itself rather than only in the caption, to improve readability.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the gGut/QSCI chain is a genuine self-consistent solver with external FCI benchmarks; self-citations are minor and non-load-bearing.

full rationale

The paper's chain is a fixed-point embedding calculation, not an input-output tautology. The gGut loop defines Hemb from quasi-particle data, solves Hemb (classically via FCI/SCI or quantumly via QSCI), and feeds the resulting 1-RDMs back into the quasi-particle Hamiltonian until convergence. This is a standard self-consistent solver: the map has nontrivial content, as shown by the fact that Ng=1 fails to capture the Mott transition while Ng>=9 does, and that the LUCJ trial state alone gives a poor DOS whereas the QSCI diagonalization in the sampled basis improves it. The LUCJ parameters are classically pre-optimized against the same embedding Hamiltonian, but the QSCI subspace diagonalization is a distinct step and is benchmarked against FCI, so the reported DOS is not the LUCJ energy or state by construction. The '1% of CI basis states' claim is an empirical truncation test, not a fitted parameter renamed as a prediction. Self-citations (Refs. [14], [19], [51], [99]) appear only in background lists or as future-outlook suggestions; the gGut formalism and LUCJ ansatz are cited to external groups, and no uniqueness theorem or ansatz is imported from the authors' own prior work. One caveat is worth flagging: the circuit-cutting reassembly in Secs. IIIC and IVB states that 'the CIs must be reassembled from the sampled partial CIs for each spin through a tensor product,' while Eq. (24) is a signed quasiprobability decomposition. The paper does not report applying the c_i signs to the CI counts or validating the reassembled distribution against an uncut simulation. This is a potential correctness and validation gap for the hardware samples, but it is not an equivalence between the paper's input and its output by construction, so it does not constitute circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The free parameters are the variational circuit parameters, the hand-chosen truncation fractions, and the fitted exponential scaling. The axioms are standard assumptions about sparsity and the representational power of the LUCJ trial state, all of which are validated empirically only for the single-band Bethe lattice Hubbard model.

free parameters (3)
  • LUCJ circuit parameters (kappa, gamma, theta, lambda, phi) = Not tabulated; optimized via SPSA with CCSD warm start.
    The LUCJ ansatz parameters are fitted to approximate the ground state of the embedding Hamiltonian for each U and Ng. They are not derived from first principles.
  • Truncation fraction p = p = 0.01, 0.05, 0.06, 0.1, 0.15, 0.3 in different runs
    The fraction of CI basis states is chosen by hand and impacts the DOS accuracy. There is no optimality criterion for p; it is selected per run.
  • Exponential fit parameters a, b, c for SCI scaling (Fig. 4d) = c=0.22 for Sigma_alpha=0.9999, c=0.30 for 0.999999
    The extrapolation of required CI count to larger N is fit to a+b exp(cN) using only three data points (Ng=7,9,11). This is a fitted scaling law, not a derived one.
assumptions (4)
  • domain assumption The ground state of the gGut embedding Hamiltonian is sufficiently sparse in the CI basis after the star-rotation.
    This is the central assumption verified empirically in Fig. 4 for the specific Bethe lattice Hubbard model. It is not proven, and the paper notes that other embedding methods (DMFT, EwDMET) may produce different sparsity.
  • domain assumption The LUCJ ansatz, with m=1 or m=3 layers, prepared on IQM hardware, yields samples whose CI distribution is good enough for QSCI to capture the low-frequency DOS.
    The paper shows that the LUCJ state itself gives poor DOS (Fig. 5), and relies on the QSCI subspace diagonalization to correct the bias. The assumption is that the span of sampled CIs is more important than the state's overlap with the ground state.
  • ad hoc to paper Circuit cutting with two wire cuts and reassembly of CI samples as tensor products preserves the relevant CI subspace.
    The paper observes that cutting improves results compared to the uncut circuit, but this is an empirical observation on a specific device and circuit, not a general theorem. The mixing of CIs from partial circuits is stated to 'effectively change the nature of the underlying quantum states.'
  • domain assumption The gGut self-consistent loop converges to a fixed point, and 11 iterations are sufficient for the studied parameters.
    The paper states that gGut convergence is difficult at Ng=11 and uses warm starts. The loop's stability under the approximate QSCI solver is demonstrated but not proven.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Quantum Assisted Ghost Gutzwiller Ansatz." pith.science (2026). https://pith.science/paper/MHE63QZK

@misc{pith2026250621431,
  author       = {Pith},
  title        = {Pith review of: Quantum Assisted Ghost Gutzwiller Ansatz},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MHE63QZK}},
  note         = {Machine review of arXiv:2506.21431}
}
abstract

The ghost Gutzwiller ansatz (gGut) embedding technique was shown to achieve comparable accuracy to the gold standard dynamical mean-field theory method in simulating real material properties, yet at a much lower computational cost. Despite that, gGut is limited by the algorithmic bottleneck of computing the density matrix of the underlying effective embedding model, a quantity which must be converged within a self-consistent embedding loop. We develop a hybrid quantum-classical gGut technique which computes the ground state properties of embedding Hamiltonians with the help of a quantum computer, using the sample-based quantum-selected configuration interaction (QSCI) algorithm. We study the applicability of SCI-based methods to the evaluation of the density of states for single-band Anderson impurity models within gGut and find that such ground states of interest become sufficiently sparse in the CI basis as the number of ghost orbitals is increased. Further, we investigate the performance of QSCI using local unitary cluster Jastrow (LUCJ) variational quantum states in combination with a circuit cutting technique, prepared on IQM's quantum hardware for system sizes of up to 11 ghost orbitals, equivalent to 24 qubits. We report converged gGut calculations which correctly capture the metal-to-insulator phase transition in the Fermi-Hubbard model on the Bethe lattice by using quantum samples to build an SCI basis with as little as $1\%$ of the total CI basis states.

Figures

Figures reproduced from arXiv: 2506.21431 by the authors.

Figure 1
Figure 1. gGut: classical and quantum workflow. Instead of attempting to directly solve the system of interest defined on a periodic lattice (a), a self-consistent loop (b) involving a quasi-particle Hamiltonian (c) and an embedding Hamiltonian (d) is executed until the density matrices of both models have converged to the same value. In our hybrid quantum-classical workflow, the ground-state density matrix of the embedding H… view at source ↗
Figure 2
Figure 2. (a) Classical gGut calculations for the single [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. SCI impurity solver for gGut. A comparison of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: (a) A comparison of the weights |α| 2 of the CIs Sx from the ground state wavefunction, ordered by their magnitude, at U = 2 and Ng = 7 is shown for different Hamiltonian bases, obtained by rotating the non-interacting part of Hˆemb (see main text). (b) For the star co…
Figure 5
Figure 5. Figure 5: Comparison of the approximate ground state DOS obtained from classically optimized and simulated LUCJ [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Comparison of the DOS computed with QSCI constructed with [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Comparison of the DOS computed with QSCI plus circuit cutting, constructed with a total [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: Comparison of the DOS obtained with QSCI plus circuit cutting, computed from [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: Comparison of the error Rn A (defined by Eq.(A1)) for different moments n of the density of states for U = 2, Ng = 9 and as a function of the fraction of the total CI states in the SCI basis, p [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: The rate of decay of CI weights as a function [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: (a) A comparison of the weights |α| 2 of CIs Sx, ordered by their magnitude from the FCI ground state wavefunction at U = 2, Ng = 7 is shown for different rotated Hamiltonian bases (see main text). Results for converged gGut embedding Hamiltonians (solid lines) are co…
Figure 12
Figure 12. Figure 12: (a) Convergence of the optimized LUCJ ansatz energy [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Comparison of the approximate ground state DOS obtained from the optimized simulated LUCJ ansatz [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: The DOS obtained from QSCI constructed with a total of [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]

Discussion (0). Sign in 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. Linear Foundation Model for Quantum Embedding: Data-Driven Compression of the Ghost Gutzwiller Variational Space

    cond-mat.str-el 2025-12 conditional novelty 6.0 of 10

    A PCA-learned linear subspace of embedding-Hamiltonian ground states solves ghost-Gutzwiller embedding problems with exact-diagonalization accuracy but orders-of-magnitude lower cost, and transfers across lattices and...

Reference graph

Works this paper leans on

108 extracted references · 66 canonical work pages · cited by 1 Pith paper

  1. [1]

    Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations.Physical review let- ters, 94(17):170201, 2005

    Matthias Troyer and Uwe-Jens Wiese. Computational complexity and fundamental limitations to fermionic quantum monte carlo simulations.Physical review let- ters, 94(17):170201, 2005

    2005

  2. [2]

    Dynamical mean-field theory of strongly correlated fermion systems and the limit of in- finite dimensions

    Antoine Georges, Gabriel Kotliar, Werner Krauth, and Marcelo J Rozenberg. Dynamical mean-field theory of strongly correlated fermion systems and the limit of in- finite dimensions. Reviews of modern physics, 68(1):13, 1996

    1996

  3. [3]

    Quantum cluster theories.Reviews of Modern Physics, 77(3):1027–1080, 2005

    Thomas Maier, Mark Jarrell, Thomas Pruschke, and Matthias H Hettler. Quantum cluster theories.Reviews of Modern Physics, 77(3):1027–1080, 2005

    2005

  4. [4]

    To- wards the practical application of near-term quan- tum computers in quantum chemistry simulations: A problem decomposition approach

    Takeshi Yamazaki, Shunji Matsuura, Ali Narimani, Anushervon Saidmuradov, and Arman Zaribafiyan. To- wards the practical application of near-term quan- tum computers in quantum chemistry simulations: A problem decomposition approach. arXiv preprint arXiv:1806.01305, 2018

    arXiv 2018

  5. [5]

    Krylov vari- ational quantum algorithm for first principles materials simulations

    Francois Jamet, Abhishek Agarwal, Carla Lupo, Dan E Browne, Cedric Weber, and Ivan Rungger. Krylov vari- ational quantum algorithm for first principles materials simulations. arXiv preprint arXiv:2105.13298, 2021

    arXiv 2021

  6. [6]

    Quantum embedding theory for strongly correlated 14 states in materials

    He Ma, Nan Sheng, Marco Govoni, and Giulia Galli. Quantum embedding theory for strongly correlated 14 states in materials. Journal of Chemical Theory and Computation, 17(4):2116–2125, 2021

    2021

  7. [7]

    Gutzwiller hybrid quantum- classical computing approach for correlated materials

    Yongxin Yao, Feng Zhang, Cai-Zhuang Wang, Kai-Ming Ho, and Peter P Orth. Gutzwiller hybrid quantum- classical computing approach for correlated materials. Physical Review Research, 3(1):013184, 2021

    2021

  8. [8]

    Pauline Besserve and Thomas Ayral. Unraveling corre- lated material properties with noisy quantum comput- ers: Natural orbitalized variational quantum eigensolv- ing of extended impurity models within a slave-boson approach. Physical Review B, 105(11):115108, 2022

    2022

Show all 108 references
  1. [9]

    Modellingcarboncap- ture on metal-organic frameworks with quantum com- puting

    Gabriel Greene-Diniz, David Zsolt Manrique, Wassil Sennane, Yann Magnin, Elvira Shishenina, Philippe Cordier, Philip Llewellyn, Michal Krompiec, Marko J Rančić, andDavidMuñozRamo. Modellingcarboncap- ture on metal-organic frameworks with quantum com- puting. EPJ Quantum Techno...

    2022

  2. [10]

    Towards accurate quantum chemical calcu- lations on noisy quantum computers

    Naoki Iijima, Satoshi Imamura, Mikio Morita, Sho Takemori, Akihiko Kasagi, Yuhei Umeda, and Eiji Yoshida. Towards accurate quantum chemical calcu- lations on noisy quantum computers. arXiv preprint arXiv:2311.09634, 2023

    2023 arXiv

  3. [11]

    Quantum algorithm for imaginary-time green’s func- tions

    Diksha Dhawan, Dominika Zgid, and Mario Motta. Quantum algorithm for imaginary-time green’s func- tions. Journal of Chemical Theory and Computation, 20(11):4629–4638, 2024

    2024

  4. [12]

    Turning qubit noise into a bless- ing: Automatic state preparation and long-time dynam- ics for impurity models on quantum computers.arXiv preprint arXiv:2412.13711, 2024

    Corentin Bertrand, Pauline Besserve, Michel Ferrero, and Thomas Ayral. Turning qubit noise into a bless- ing: Automatic state preparation and long-time dynam- ics for impurity models on quantum computers.arXiv preprint arXiv:2412.13711, 2024

    2024 arXiv

  5. [13]

    Towards quantum-centric simulations of ex- tended molecules: sample-based quantum diagonaliza- tion enhanced with density matrix embedding theory

    Akhil Shajan, Danil Kaliakin, Abhishek Mitra, Javier Robledo Moreno, Zhen Li, Mario Motta, Caleb Johnson, AbdullahAshSaki, SusantaDas, IskandarSit- dikov, et al. Towards quantum-centric simulations of ex- tended molecules: sample-based quantum diagonaliza- tion enhanced with d...

    2024 arXiv

  6. [14]

    Anderson impurity solver integrating tensor net- work methods with quantum computing

    François Jamet, Lachlan P Lindoy, Yannic Rath, Con- nor Lenihan, Abhishek Agarwal, Enrico Fontana, Fedor Simkovic, Baptiste Anselme Martin, and Ivan Rung- ger. Anderson impurity solver integrating tensor net- work methods with quantum computing. APL Quan- tum, 2(1), 2025

    2025

  7. [15]

    Variational quantum-algorithm based self- consistent calculations for the two-site dmft model on noisy quantum computing hardware

    Jannis Ehrlich, Daniel F Urban, and Christian El- sässer. Variational quantum-algorithm based self- consistent calculations for the two-site dmft model on noisy quantum computing hardware. Journal of Physics: Condensed Matter, 2025

    2025

  8. [16]

    Green’s function formulation of quantum de- fect embedding theory.Journal of Chemical Theory and Computation, 18(6):3512–3522, 2022

    Nan Sheng, Christian Vorwerk, Marco Govoni, and Giu- lia Galli. Green’s function formulation of quantum de- fect embedding theory.Journal of Chemical Theory and Computation, 18(6):3512–3522, 2022

    2022

  9. [17]

    Self-energy embedding the- ory (seet) for periodic systems.Journal of chemical the- ory and computation, 15(1):229–240, 2018

    Alexander A Rusakov, Sergei Iskakov, Lan Nguyen Tran, and Dominika Zgid. Self-energy embedding the- ory (seet) for periodic systems.Journal of chemical the- ory and computation, 15(1):229–240, 2018

    2018

  10. [18]

    Generalized self- energy embedding theory.The journal of physical chem- istry letters, 8(10):2200–2205, 2017

    Tran Nguyen Lan and Dominika Zgid. Generalized self- energy embedding theory.The journal of physical chem- istry letters, 8(10):2200–2205, 2017

    2017

  11. [19]

    Jules Tilly, P. V. Sriluckshmy, Akashkumar Patel, En- rico Fontana, Ivan Rungger, Edward Grant, Robert An- derson, Jonathan Tennyson, and George H. Booth. Re- duced density matrix sampling: Self-consistent embed- ding and multiscale electronic structure on current gen- eration...

    2021

  12. [20]

    Quantum embedding theories

    Qiming Sun and Garnet Kin-Lic Chan. Quantum embedding theories. Accounts of chemical research, 49(12):2705–2712, 2016

    2016

  13. [21]

    Quantum embedding theories to simulate condensed systems on quantum computers

    Christian Vorwerk, Nan Sheng, Marco Govoni, Benchen Huang, and Giulia Galli. Quantum embedding theories to simulate condensed systems on quantum computers. Nature Computational Science, 2(7):424–432, 2022

    2022

  14. [22]

    Dynamical cluster approxima- tion: Nonlocal dynamics of correlated electron systems

    Mattias H Hettler, M Mukherjee, Mark Jarrell, and Hu- likal R Krishnamurthy. Dynamical cluster approxima- tion: Nonlocal dynamics of correlated electron systems. Physical Review B, 61(19):12739, 2000

    2000

  15. [23]

    Cellular dynamical mean field ap- proach to strongly correlated systems.Physical review letters, 87(18):186401, 2001

    Gabriel Kotliar, Sergej Y Savrasov, Gunnar Pálsson, and Giulio Biroli. Cellular dynamical mean field ap- proach to strongly correlated systems.Physical review letters, 87(18):186401, 2001

    2001

  16. [24]

    Diagrammatic routes to nonlocal correlations beyond dynamical mean field theory

    G Rohringer, H Hafermann, A Toschi, AA Katanin, AE Antipov, MI Katsnelson, AI Lichtenstein, AN Rubtsov, and K Held. Diagrammatic routes to nonlocal correlations beyond dynamical mean field theory. Reviews of Modern Physics, 90(2):025003, 2018

    2018

  17. [25]

    Real-space cluster dynamical mean-field theory: Center-focused extrapolation on the one-and two particle-levels

    Marcel Klett, Nils Wentzell, Thomas Schäfer, Fedor Simkovic IV, Olivier Parcollet, Sabine Andergassen, and Philipp Hansmann. Real-space cluster dynamical mean-field theory: Center-focused extrapolation on the one-and two particle-levels. Physical Review Research, 2(3):033476, 2020

    2020

  18. [26]

    The lda+ dmft approach to strongly correlated materials

    Eva Pavarini, Dieter Vollhardt, Erik Koch, and Alexan- der Lichtenstein. The lda+ dmft approach to strongly correlated materials. Technical report, Theoretische Na- noelektronik, 2011

    2011

  19. [27]

    Realistic modeling of strongly correlated electron systems: An introduction to the lda+ dmft approach

    Karsten Held, Igor A Nekrasov, Nils Blümer, VI Anisi- mov, and Dieter Vollhardt. Realistic modeling of strongly correlated electron systems: An introduction to the lda+ dmft approach. International Journal of Modern Physics B, 15(19n20):2611–2625, 2001

    2001

  20. [28]

    Extended dynamical mean-field theory and gw method.Physical Review B, 66(8):085120, 2002

    Ping Sun and Gabriel Kotliar. Extended dynamical mean-field theory and gw method.Physical Review B, 66(8):085120, 2002

    2002

  21. [29]

    When strong correlations become weak: Consistent merging of gw and dmft

    Lewin Boehnke, Fredrik Nilsson, Ferdi Aryasetiawan, and Philipp Werner. When strong correlations become weak: Consistent merging of gw and dmft. Physical Review B, 94(20):201106, 2016

    2016

  22. [30]

    Ab initio full cell gw+ dmft for correlated materials.Physical Review X, 11(2):021006, 2021

    Tianyu Zhu and Garnet Kin-Lic Chan. Ab initio full cell gw+ dmft for correlated materials.Physical Review X, 11(2):021006, 2021

    2021

  23. [31]

    Continuous-time monte carlo methods for quantum impurity models.Reviews of Modern Physics, 83(2):349–404, 2011

    Emanuel Gull, Andrew J Millis, Alexander I Lichten- stein, Alexey N Rubtsov, Matthias Troyer, and Philipp Werner. Continuous-time monte carlo methods for quantum impurity models.Reviews of Modern Physics, 83(2):349–404, 2011

    2011

  24. [32]

    Solving the eliashberg equationsbymeansofn-pointpadéapproximants

    HJ Vidberg and JW Serene. Solving the eliashberg equationsbymeansofn-pointpadéapproximants. Jour- nal of Low Temperature Physics, 29:179–192, 1977

    1977

  25. [33]

    Nevanlinna analytical continuation

    Jiani Fei, Chia-Nan Yeh, and Emanuel Gull. Nevanlinna analytical continuation. Phys. Rev. Lett., 126:056402, Feb 2021

    2021

  26. [34]

    The metal–insulator transition in the hubbard model at zero temperature ii

    Marcelo J Rozenberg, Goetz Moeller, and Gabriel Kotliar. The metal–insulator transition in the hubbard model at zero temperature ii. Modern Physics Letters B, 8(08n09):535–543, 1994

    1994

  27. [35]

    Exact diagonaliza- 15 tion approach to correlated fermions in infinite dimen- sions: Mott transition and superconductivity.Physical review letters, 72(10):1545, 1994

    Michel Caffarel and Werner Krauth. Exact diagonaliza- 15 tion approach to correlated fermions in infinite dimen- sions: Mott transition and superconductivity.Physical review letters, 72(10):1545, 1994

    1994

  28. [36]

    Truncated configuration interaction expan- sions as solvers for correlated quantum impurity mod- els and dynamical mean-field theory

    Dominika Zgid, Emanuel Gull, and Garnet Kin-Lic Chan. Truncated configuration interaction expan- sions as solvers for correlated quantum impurity mod- els and dynamical mean-field theory. Physical Re- view B—Condensed Matter and Materials Physics , 86(16):165128, 2012

    2012

  29. [37]

    Chungwei Lin and Alexander A Demkov. Efficient vari- ational approach to the impurity problem and its ap- plication to the dynamical mean-field theory.Physical Review B—Condensed Matter and Materials Physics, 88(3):035123, 2013

    2013

  30. [38]

    Efficient dmft impurity solver using real-time dynam- ics with matrix product states

    Martin Ganahl, Markus Aichhorn, Hans Gerd Evertz, Patrik Thunström, Karsten Held, and Frank Verstraete. Efficient dmft impurity solver using real-time dynam- ics with matrix product states. Physical Review B, 92(15):155132, 2015

    2015

  31. [39]

    Fork tensor- product states: Efficient multiorbital real-time dmft solver

    Daniel Bauernfeind, Manuel Zingl, Robert Triebl, Markus Aichhorn, and Hans Gerd Evertz. Fork tensor- product states: Efficient multiorbital real-time dmft solver. Physical Review X, 7(3):031013, 2017

    2017

  32. [40]

    Tree tensor-network real-time multior- bital impurity solver: Spin-orbit coupling and corre- lation functions in sr 2 ruo 4

    Xiaodong Cao, Yi Lu, P Hansmann, and Maurits W Haverkort. Tree tensor-network real-time multior- bital impurity solver: Spin-orbit coupling and corre- lation functions in sr 2 ruo 4. Physical Review B, 104(11):115119, 2021

    2021

  33. [41]

    Finite-temperature minimally entangled typi- cal thermal states impurity solver.Physical Review B, 109(24):245113, 2024

    Xiaodong Cao, E Miles Stoudenmire, and Olivier Par- collet. Finite-temperature minimally entangled typi- cal thermal states impurity solver.Physical Review B, 109(24):245113, 2024

    2024

  34. [42]

    Den- sity matrix embedding: A simple alternative to dy- namical mean-field theory

    Gerald Knizia and Garnet Kin-Lic Chan. Den- sity matrix embedding: A simple alternative to dy- namical mean-field theory. Physical review letters, 109(18):186404, 2012

    2012

  35. [43]

    Density ma- trix embedding: A strong-coupling quantum embedding theory

    Gerald Knizia and Garnet Kin-Lic Chan. Density ma- trix embedding: A strong-coupling quantum embedding theory. Journal of chemical theory and computation, 9(3):1428–1432, 2013

    2013

  36. [44]

    A practical guide to density matrix embedding theory in quantum chemistry.Jour- nal of chemical theory and computation, 12(6):2706– 2719, 2016

    Sebastian Wouters, Carlos A Jiménez-Hoyos, Qiming Sun, and Garnet K-L Chan. A practical guide to density matrix embedding theory in quantum chemistry.Jour- nal of chemical theory and computation, 12(6):2706– 2719, 2016

    2016

  37. [45]

    Efficient implementation of the gutzwiller varia- tional method

    Nicola Lanata, Hugo UR Strand, Xi Dai, and Bo Hells- ing. Efficient implementation of the gutzwiller varia- tional method. Physical Review B—Condensed Matter and Materials Physics, 85(3):035133, 2012

    2012

  38. [46]

    Critical role of electronic correlations in determining crystal struc- ture of transition metal compounds

    Nicola Lanata, Tsung-Han Lee, Yong-Xin Yao, Vladan Stevanović, and Vladimir Dobrosavljević. Critical role of electronic correlations in determining crystal struc- ture of transition metal compounds. arXiv preprint arXiv:1710.08586, 2017

    arXiv

  39. [47]

    Connection between mott physics and crystal structure in a series of transition metal binary compounds.npj Computational Materials, 5(1):30, 2019

    Nicola Lanatà, Tsung-Han Lee, Yong-Xin Yao, Vladan Stevanović, and Vladimir Dobrosavljević. Connection between mott physics and crystal structure in a series of transition metal binary compounds.npj Computational Materials, 5(1):30, 2019

    2019

  40. [48]

    Dynamical mean-field theory, density-matrix embed- ding theory, and rotationally invariant slave bosons: A unified perspective

    Thomas Ayral, Tsung-Han Lee, and Gabriel Kotliar. Dynamical mean-field theory, density-matrix embed- ding theory, and rotationally invariant slave bosons: A unified perspective. Physical Review B, 96(23):235139, 2017

    2017

  41. [49]

    Accuracy of ghost rotationally invariant slave-boson and dynamical mean field theory as a function of the impurity-model bath size

    Tsung-Han Lee, Nicola Lanatà, and Gabriel Kotliar. Accuracy of ghost rotationally invariant slave-boson and dynamical mean field theory as a function of the impurity-model bath size. Physical Review B, 107(12):L121104, 2023

    2023

  42. [50]

    Derivation of the ghost gutzwiller approximation from quantum embedding principles: Ghost density matrix embedding theory.Physical Re- view B, 108(23):235112, 2023

    Nicola Lanatà. Derivation of the ghost gutzwiller approximation from quantum embedding principles: Ghost density matrix embedding theory.Physical Re- view B, 108(23):235112, 2023

    2023

  43. [51]

    P. V. Sriluckshmy, Max Nusspickel, Edoardo Fertitta, andGeorgeH.Booth. Fullyalgebraicandself-consistent effective dynamics in a static quantum embedding. Phys. Rev. B, 103:085131, Feb 2021

    2021

  44. [52]

    Operatorial formulation of the ghost ro- tationally invariant slave-boson theory.Physical Review B, 105(4):045111, 2022

    Nicola Lanatà. Operatorial formulation of the ghost ro- tationally invariant slave-boson theory.Physical Review B, 105(4):045111, 2022

    2022

  45. [53]

    Accuracy of ghost- rotationally-invariant slave-boson theory for multior- bital hubbard models and realistic materials.Physical Review B, 108(24):245147, 2023

    Tsung-Han Lee, Corey Melnick, Ran Adler, Nicola Lanatà, and Gabriel Kotliar. Accuracy of ghost- rotationally-invariant slave-boson theory for multior- bital hubbard models and realistic materials.Physical Review B, 108(24):245147, 2023

    2023

  46. [54]

    Effi- cient computational screening of strongly correlated materials: Multiorbital phenomenology within the ghost gutzwiller approximation

    Carlos Mejuto-Zaera and Michele Fabrizio. Effi- cient computational screening of strongly correlated materials: Multiorbital phenomenology within the ghost gutzwiller approximation. Physical Review B, 107(23):235150, 2023

    2023

  47. [55]

    Charge self-consistent density functional theory plus ghost rotationally invariant slave-boson theory for cor- related materials

    Tsung-Han Lee, Corey Melnick, Ran Adler, Xue Sun, Yongxin Yao, Nicola Lanatà, and Gabriel Kotliar. Charge self-consistent density functional theory plus ghost rotationally invariant slave-boson theory for cor- related materials. Physical Review B, 110(11):115126, 2024

    2024

  48. [56]

    Active learning approach to simulations of strongly correlated matter with the ghost gutzwiller approximation

    Marius S Frank, Denis G Artiukhin, Tsung-Han Lee, Yongxin Yao, Kipton Barros, Ove Christiansen, and Nicola Lanatà. Active learning approach to simulations of strongly correlated matter with the ghost gutzwiller approximation. Physical Review Research, 6(1):013242, 2024

    2024

  49. [57]

    Revealing spinons by proximity ef- fect

    Antonio Maria Tagliente, Carlos Mejuto-Zaera, and Michele Fabrizio. Revealing spinons by proximity ef- fect. Physical Review B, 111(12):125110, 2025

    2025

  50. [58]

    Quantum embedding for molecules using auxiliary particles–the ghost gutzwiller ansatz

    Carlos Mejuto-Zaera. Quantum embedding for molecules using auxiliary particles–the ghost gutzwiller ansatz. Faraday Discussions, 254:653–681, 2024

    2024

  51. [59]

    Density matrix formulation for quan- tum renormalization groups

    Steven R White. Density matrix formulation for quan- tum renormalization groups. Physical review letters, 69(19):2863, 1992

    1992

  52. [60]

    Block2: A comprehensive open source framework to develop and apply state-of-the-art dmrg algorithms in electronic structureandbeyond

    Huanchen Zhai, Henrik R Larsson, Seunghoon Lee, Zhi- Hao Cui, Tianyu Zhu, Chong Sun, Linqing Peng, Ruo- jing Peng, Ke Liao, Johannes Tölle, et al. Block2: A comprehensive open source framework to develop and apply state-of-the-art dmrg algorithms in electronic structureandbeyo...

    2023

  53. [61]

    Iterative per- turbation calculations of ground and excited state en- ergies from multiconfigurational zeroth-order wavefunc- tions

    B Huron, J P Malrieu, and P Rancurel. Iterative per- turbation calculations of ground and excited state en- ergies from multiconfigurational zeroth-order wavefunc- tions. J. Chem. Phys., v. 58, no. 12, pp. 5745-5759, 06 1973

    1973

  54. [62]

    Selected con- figuration interaction method using sampled first-order corrections to wave functions.The Journal of Chemical Physics, 147(3), 2017

    Yuhki Ohtsuka and Jun-ya Hasegawa. Selected con- figuration interaction method using sampled first-order corrections to wave functions.The Journal of Chemical Physics, 147(3), 2017

    2017

  55. [63]

    Casscf with extremely large active spaces using the adaptive sampling configuration interaction method

    Daniel S Levine, Diptarka Hait, Norm M Tubman, 16 Susi Lehtola, K Birgitta Whaley, and Martin Head- Gordon. Casscf with extremely large active spaces using the adaptive sampling configuration interaction method. Journal of chemical theory and computation, 16(4):2340–2354, 2020

    2020

  56. [64]

    Quantum-selected configuration interac- tion: Classical diagonalization of hamiltonians in sub- spaces selected by quantum computers.arXiv preprint arXiv:2302.11320, 2023

    Keita Kanno, Masaya Kohda, Ryosuke Imai, Sho Koh, Kosuke Mitarai, Wataru Mizukami, and Yuya O Nakagawa. Quantum-selected configuration interac- tion: Classical diagonalization of hamiltonians in sub- spaces selected by quantum computers.arXiv preprint arXiv:2302.11320, 2023

    2023 arXiv

  57. [65]

    Adapt- qsci: Adaptive construction of an input state for quantum-selected configuration interaction

    Yuya O Nakagawa, Masahiko Kamoshita, Wataru Mizukami, Shotaro Sudo, and Yu-ya Ohnishi. Adapt- qsci: Adaptive construction of an input state for quantum-selected configuration interaction. Journal of Chemical Theory and Computation, 20(24):10817– 10825, 2024

    2024

  58. [66]

    Quantum- selected configuration interaction with time-evolved state

    Mathias Mikkelsen and Yuya O Nakagawa. Quantum- selected configuration interaction with time-evolved state. arXiv preprint arXiv:2412.13839, 2024

    2024

  59. [67]

    Doney, Javier Rob- ledo Moreno, Joshua A

    Ieva Liepuoniute, Kirstin D. Doney, Javier Rob- ledo Moreno, Joshua A. Job, William S. Friend, and Gavin O. Jones. Quantum-centric computational study of methylene singlet and triplet states. Journal of Chemical Theory and Computation, 0(0):null, 0. PMID: 40357738

  60. [68]

    Sample-based krylov quantum diagonaliza- tion

    Jeffery Yu, Javier Robledo Moreno, Joseph Iosue, Luke Bertels, Daniel Claudino, Bryce Fuller, Peter Groszkowski, Travis S Humble, Petar Jurcevic, William Kirby, et al. Sample-based krylov quantum diagonaliza- tion. arXiv e-prints, pages arXiv–2501, 2025

    2025

  61. [69]

    Bridging physical intu- ition and hardware efficiency for correlated electronic states: the local unitary cluster jastrow ansatz for elec- tronicstructure

    Mario Motta, Kevin J Sung, K Birgitta Whaley, Martin Head-Gordon, and James Shee. Bridging physical intu- ition and hardware efficiency for correlated electronic states: the local unitary cluster jastrow ansatz for elec- tronicstructure. Chemical Science, 14(40):11213–11227, 2023

    2023

  62. [70]

    Technology and performance bench- marks of iqm’s 20-qubit quantum computer

    Leonid Abdurakhimov, Janos Adam, Hasnain Ahmad, Olli Ahonen, Manuel Algaba, Guillermo Alonso, Ville Bergholm, Rohit Beriwal, Matthias Beuerle, Clinton Bockstiegel, et al. Technology and performance bench- marks of iqm’s 20-qubit quantum computer. arXiv preprint arXiv:2408.12433, 2024

    2024 arXiv

  63. [71]

    Simulating large quantum circuits on a small quan- tum computer

    Tianyi Peng, Aram W Harrow, Maris Ozols, and Xiaodi Wu. Simulating large quantum circuits on a small quan- tum computer. Physical review letters, 125(15):150504, 2020

    2020

  64. [72]

    Fast quantum circuit cutting with randomized measurements

    Angus Lowe, Matija Medvidović, Anthony Hayes, Lee J O’Riordan, Thomas R Bromley, Juan Miguel Arrazola, and Nathan Killoran. Fast quantum circuit cutting with randomized measurements. Quantum, 7:934, 2023

    2023

  65. [73]

    Hopping on the bethe lattice: Exact results for densities of states and dynamical mean-field theory

    Martin Eckstein, Marcus Kollar, Krzysztof Byczuk, and Dieter Vollhardt. Hopping on the bethe lattice: Exact results for densities of states and dynamical mean-field theory. Phys. Rev. B, 71:235119, Jun 2005

    2005

  66. [74]

    Frank, Tsung-Han Lee, Gargee Bhat- tacharyya, Pak Ki Henry Tsang, Victor L

    Marius S. Frank, Tsung-Han Lee, Gargee Bhat- tacharyya, Pak Ki Henry Tsang, Victor L. Quito, Vladimir Dobrosavljević, Ove Christiansen, and Nicola Lanatà. Quantum embedding description of the ander- son lattice model with the ghost gutzwiller approxima- tion. Phys. Rev. B, 104...

    2021

  67. [75]

    Distributed implementation of full configura- tion interaction for one trillion determinants.Journal of Chemical Theory and Computation, 20(3):1185–1192, 2024

    Hong Gao, Satoshi Imamura, Akihiko Kasagi, and Eiji Yoshida. Distributed implementation of full configura- tion interaction for one trillion determinants.Journal of Chemical Theory and Computation, 20(3):1185–1192, 2024

    2024

  68. [76]

    Heat-bath configuration interaction: An efficient selected configuration interaction algorithm inspired by heat-bath sampling

    Adam A Holmes, Norm M Tubman, and CJ Umri- gar. Heat-bath configuration interaction: An efficient selected configuration interaction algorithm inspired by heat-bath sampling. Journal of chemical theory and computation, 12(8):3674–3680, 2016

    2016

  69. [77]

    Convergence of an improved cipsi algorithm

    Stefano Evangelisti, Jean-Pierre Daudey, and Jean-Paul Malrieu. Convergence of an improved cipsi algorithm. Chemical Physics, 75(1):91–102, 1983

    1983

  70. [78]

    The performance of cipsi on the ground state electronic energy of benzene.The Journal of Chemical Physics, 153(17):176101, 11 2020

    Pierre-François Loos, Yann Damour, and Anthony Sce- mama. The performance of cipsi on the ground state electronic energy of benzene.The Journal of Chemical Physics, 153(17):176101, 11 2020

    2020

  71. [79]

    Exposing a fatal flaw in sample-based quantum diagonalization methods

    Peter Reinholdt, Karl Michael Ziems, Erik Rosendahl Kjellgren, Sonia Coriani, Stephan Sauer, and Jacob Kongsted. Exposing a fatal flaw in sample-based quantum diagonalization methods. arXiv preprint arXiv:2501.07231, 2025

    2025 arXiv

  72. [80]

    Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method

    Mario Motta, Kevin J Sung, and James Shee. Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method. The Journal of Physical Chemistry A, 128(40):8762–8776, 2024

    2024

  73. [81]

    Coupled-cluster theory in quantum chemistry

    Rodney J Bartlett and Monika Musiał. Coupled-cluster theory in quantum chemistry. Reviews of Modern Physics, 79(1):291–352, 2007

    2007

  74. [82]

    Constructing a vir- tual two-qubit gate by sampling single-qubit operations

    Kosuke Mitarai and Keisuke Fujii. Constructing a vir- tual two-qubit gate by sampling single-qubit operations. New Journal of Physics, 23(2):023021, 2021

    2021

  75. [83]

    Optimal wire cutting with classical communication

    Lukas Brenner, Christophe Piveteau, and David Sut- ter. Optimal wire cutting with classical communication. arXiv preprint arXiv:2302.03366, 2023

    2023

  76. [84]

    Quasiprobability decompositions with reduced sampling overhead.npj Quantum Information, 8(1):12, 2022

    Christophe Piveteau, David Sutter, and Stefan Wo- erner. Quasiprobability decompositions with reduced sampling overhead.npj Quantum Information, 8(1):12, 2022

    2022

  77. [85]

    Estimating outcome probabilities of quantum circuits using quasiprobabilities.Physical review letters, 115(7):070501, 2015

    Hakop Pashayan, Joel J Wallman, and Stephen D Bartlett. Estimating outcome probabilities of quantum circuits using quasiprobabilities.Physical review letters, 115(7):070501, 2015

    2015

  78. [86]

    Doubly optimal parallel wire cutting without ancilla qubits

    Hiroyuki Harada, Kaito Wada, and Naoki Yamamoto. Doubly optimal parallel wire cutting without ancilla qubits. PRX Quantum, 5:040308, Oct 2024

    2024

  79. [87]

    https://docs.quantum.ibm.com/guides/qiskit- addons-cutting

    Circuitcutting. https://docs.quantum.ibm.com/guides/qiskit- addons-cutting

  80. [88]

    Investigating the effect of circuit cutting in qaoa for the maxcut problem on nisq devices.Quan- tum Science and Technology, 8(4):045022, 2023

    Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl, Julian Obst, Felix Truger, and Ben- jamin Weder. Investigating the effect of circuit cutting in qaoa for the maxcut problem on nisq devices.Quan- tum Science and Technology, 8(4):045022, 2023

    2023

  81. [89]

    Optimal joint cutting of two-qubit rotation gates

    Christian Ufrecht, Laura S Herzog, Daniel D Scherer, ManiramanPeriyasamy, SebastianRietsch, AxelPlinge, and Christopher Mutschler. Optimal joint cutting of two-qubit rotation gates. Physical Review A , 109(5):052440, 2024

    2024

  82. [90]

    Natural-orbital impurity solver and projection ap- proach for green’s functions

    Y Lu, X Cao, P Hansmann, and MW Haverkort. Natural-orbital impurity solver and projection ap- proach for green’s functions. Physical Review B , 100(11):115134, 2019

    2019

  83. [91]

    Efficient mapping for anderson impurity problems with matrix product states

    Lucas Kohn and Giuseppe E Santoro. Efficient mapping for anderson impurity problems with matrix product states. Physical Review B, 104(1):014303, 2021

    2021

  84. [92]

    A variational eigenvalue solver on a photonic quantum processor.Na- ture communications, 5(1):4213, 2014

    Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, 17 Man-Hong Yung, Xiao-Qi Zhou, Peter J Love, Alán Aspuru-Guzik, and Jeremy L O’brien. A variational eigenvalue solver on a photonic quantum processor.Na- ture communications, 5(1):4213, 2014

    2014

  85. [93]

    Benjamin, Sug- uru Endo, William J

    Zhenyu Cai, Ryan Babbush, Simon C. Benjamin, Sug- uru Endo, William J. Huggins, Ying Li, Jarrod R. Mc- Clean, and Thomas E. O’Brien. Quantum error mitiga- tion. Rev. Mod. Phys., 95:045005, Dec 2023

    2023

  86. [94]

    An overview of the simultaneous pertur- bation method for efficient optimization.Johns Hopkins apl technical digest, 19(4):482–492, 1998

    James C Spall. An overview of the simultaneous pertur- bation method for efficient optimization.Johns Hopkins apl technical digest, 19(4):482–492, 1998

    1998

  87. [95]

    Time-dependent ghost gutzwiller nonequilibrium dy- namics

    Daniele Guerci, Massimo Capone, and Nicola Lanatà. Time-dependent ghost gutzwiller nonequilibrium dy- namics. Physical Review Research, 5(3):L032023, 2023

    2023

  88. [96]

    Cutting circuits with multiple two-qubit unitaries

    Lukas Schmitt, Christophe Piveteau, and David Sut- ter. Cutting circuits with multiple two-qubit unitaries. Quantum, 9:1634, 2025

    2025

  89. [97]

    Overhead-constrained circuit knitting for varia- tional quantum dynamics.Quantum, 8:1296, 2024

    Gian Gentinetta, Friederike Metz, and Giuseppe Car- leo. Overhead-constrained circuit knitting for varia- tional quantum dynamics.Quantum, 8:1296, 2024

    2024

  90. [98]

    Quantum error mit- igation for sampling algorithms

    Kecheng Liu and Zhenyu Cai. Quantum error mit- igation for sampling algorithms. arXiv preprint arXiv:2502.11285, 2025

    2025 arXiv

  91. [99]

    Operator-projectedvariationalquantumimaginarytime evolution

    Aeishah Ameera Anuar, François Jamet, Fabio Gironella, Fedor Simkovic IV, and Riccardo Rossi. Operator-projectedvariationalquantumimaginarytime evolution. arXiv preprint arXiv:2409.12018, 2024

    2024 arXiv

  92. [100]

    Double-bracket quantum algorithms for quantum imaginary-time evo- lution

    Marek Gluza, Jeongrak Son, Bi Hong Tiang, Yudai Suzuki, Zoë Holmes, and Nelly HY Ng. Double-bracket quantum algorithms for quantum imaginary-time evo- lution. arXiv preprint arXiv:2412.04554, 2024

    2024

  93. [101]

    Barren plateaus in variational quantum com- puting

    Martin Larocca, Supanut Thanasilp, Samson Wang, Kunal Sharma, Jacob Biamonte, Patrick J Coles, Lukasz Cincio, Jarrod R McClean, Zoë Holmes, and M Cerezo. Barren plateaus in variational quantum com- puting. Nature Reviews Physics, pages 1–16, 2025

    2025

  94. [102]

    Simulation of fermionic circuits using majorana propagation

    Aaron Miller, Zoë Holmes, Özlem Salehi, Rahul Chakraborty, Anton Nykänen, Zoltán Zimborás, Adam Glos, and Guillermo García-Pérez. Simulation of fermionic circuits using majorana propagation. arXiv preprint arXiv:2503.18939, 2025

    2025

  95. [103]

    Quspin: a python packagefordynamicsandexactdiagonalisationofquan- tum many body systems part i: spin chains

    Phillip Weinberg and Marin Bukov. Quspin: a python packagefordynamicsandexactdiagonalisationofquan- tum many body systems part i: spin chains. SciPost Physics, 2(1):003, 2017

    2017

  96. [104]

    Re- cent developments in the pyscf program package.The Journal of Chemical Physics, 153(2), 2020

    Qiming Sun, Xing Zhang, Samragni Banerjee, Peng Bao, Marc Barbry, Nick S Blunt, Nikolay A Bogdanov, George H Booth, Jia Chen, Zhi-Hao Cui, et al. Re- cent developments in the pyscf program package.The Journal of Chemical Physics, 153(2), 2020

    2020

  97. [105]

    ffsim: Faster simulations of fermionic quantum circuits

    The ffsim developers. ffsim: Faster simulations of fermionic quantum circuits

  98. [106]

    Quantum-classical embedding via ghost gutzwiller ap- proximation for enhanced simulations of correlated elec- tron systems

    I Chen, Aleksei Khindanov, Carlos Salazar, Hum- berto Munoz Barona, Feng Zhang, Cai-Zhuang Wang, Thomas Iadecola, Nicola Lanatà, and Yong-Xin Yao. Quantum-classical embedding via ghost gutzwiller ap- proximation for enhanced simulations of correlated elec- tron systems. arXiv ...

    2025 arXiv

  99. [107]

    In total,4 macro-iterations with 10 micro iterations of the SPSA were executed for every loop iteration of gGut

    for aU close to the phase transition did not always provide good convergence and instead small angles for the rotational gates were used as a starting point for the SPSA optimization. In total,4 macro-iterations with 10 micro iterations of the SPSA were executed for every loop...

  100. [108]

    as a function of the interaction strengthU. Natu- rally, the decay rate of CI weights does not depend on the values ofU for the generic model, which shows that such a model is not able to capture the features of a self- consistent embedding calculation like gGut. We also see t...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.