Hybrid Quantum-Classical Simulations of Graphene Analogues: Adsorption Energetics Beyond DFT

Archith Rayabharam; N. R. Aluru

REVIEW 4 major objections 5 minor 17 references

Hybrid Quantum-Classical Simulations of Graphene Analogues: Adsorption Energetics Beyond DFT

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A hybrid quantum-classical framework aims to match CCSD-quality adsorption energies on graphene analogues, where DFT misses by about 1 eV.

desk verdict Useful benzine benchmark, but the 'chemically accurate' claim for coronene is unsupported and the fragment active spaces are never specified. read the letter →

arxiv 2508.21325 v1 pith:DTKXONUH submitted 2025-08-29 physics.chem-ph cond-mat.str-elquant-ph

Archith Rayabharam , N. R. Aluru This is my paper · ORCID

classification physics.chem-phcond-mat.str-elquant-ph PACS 03.67.Ac31.15.A71.15.Mb

keywords VQEMCSCFhybridquantum-classicalsimulationadsorptionenergeticsgrapheneanaloguescoronenetransition-metalbindingstrongcorrelation

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 tries to show that a hybrid quantum-classical electronic-structure method can deliver near-coupled-cluster accuracy for adsorption on graphene analogues without a full classical correlated calculation. The method, MCSCF-VQE, splits each system into a small active space of six spatial orbitals and eight electrons, solved with a variational quantum eigensolver using a UCCSD ansatz, embedded in a mean-field environment. On benzene, binding energies for Fe, Co, Ni, and water track CCSD within roughly 0.1–0.2 eV, while GGA-DFT overbinds the metals by about 1 eV and HF gives repulsive values. On coronene, where no CCSD reference is available, the method predicts Fe and Co chemisorb at about −1.8 eV while DFT calls them unbound, and finds a large vacancy-driven strengthening to about −6 to −8.6 eV. If correct, this demonstrates that small, physically motivated active spaces on near-term quantum solvers can resolve charge-transfer and multireference effects that standard DFT misses in catalytically relevant carbon systems.

What carries the argument

The central object is the MCSCF-VQE workflow: a fragmentation scheme that selects a compact active space (6 spatial orbitals, 8 electrons) from DFT orbital analyses, builds a second-quantized effective Hamiltonian, maps it to qubits with a Jordan-Wigner transform, and solves it with a UCCSD variational ansatz using a COBYLA classical optimizer and M3 readout-error mitigation. The active space is the load-bearing piece: it must contain the orbitals responsible for metal–carbon bonding, charge transfer, and multireference character, leaving the rest of the system at a mean-field level. Binding energies are then formed by subtracting separately computed fragment energies, so consistency of the

What would settle it

Run a high-level multireference benchmark (for example, CASPT2, NEVPT2, or DMRG with a basis larger than STO-6G) for Fe, Co, and Ni on pristine and single-vacancy coronene at the same geometries, and compare binding energies. If the six-orbital MCSCF-VQE values deviate by more than roughly 0.2 eV, or if the high-level calculation places Fe or Co on pristine coronene near zero binding, the central claim of chemically accurate predictions on coronene would fail.

Watch

Extended reading notes

Core claim

The central claim is that MCSCF-VQE, with an active space of six spatial orbitals and eight electrons selected from DFT density-difference and orbital analyses, achieves chemically accurate adsorption energies for water and late-3d transition metals on graphene analogues. On benzene, the method reproduces CCSD binding energies for water to within 0.015 eV and for Fe, Co, Ni to within roughly 0.11, 0.12, and 0.19 eV, respectively, while GGA-DFT overbinds the metals by about 1 eV and HF predicts spurious repulsion. On coronene, the same framework predicts Fe and Co to bind near −1.8 eV on the pristine surface, in contrast to DFT's positive binding energies, and predicts a sharp increase to abo

Load-bearing premise

The method's accuracy rests on the assumption that a hand-picked active space of six orbitals and eight electrons, taken from DFT orbital analyses and not orbital-optimized, contains the physics that sets the binding energies on coronene; the paper does not check that assumption against a high-level calculation for coronene.

Editorial extensions

If this is right

MCSCF-VQE reproduces CCSD binding energies for Fe, Co, and Ni on benzene within 0.11–0.19 eV, making it a viable substitute for expensive post-Hartree-Fock references in small metal–aromatic prototypes.
On pristine coronene, DFT's prediction that Fe and Co are unbound is contradicted by MCSCF-VQE chemisorption energies near −1.8 eV, indicating a qualitative failure of standard DFT for these systems.
Single-vacancy coronene strengthens metal binding to roughly −6.2 to −8.6 eV, with the trend Fe > Co > Ni following the metals' electron affinities, implying defect engineering can tune adsorption in sp2 carbon.
Water remains weakly physisorbed on both pristine and defective coronene (−0.12 to −0.11 eV), showing that the framework separates dispersion-dominated from charge-transfer-dominated binding.
With COBYLA optimization and M3 error mitigation, noisy VQE simulations recover near-ideal dissociation curves, suggesting the workflow is compatible with current noisy quantum hardware simulators.

Reading between the lines

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

If the benzene-to-coronene transfer holds under high-level benchmarks, the same six-orbital embedded active-space recipe could be applied to doped or multi-vacancy carbon surfaces, where DFT errors are likely similar; the vacancy binding energies predict that single vacancies will act as strong trapping sites for single-atom catalysts.
Because the active-space orbitals come from DFT and are not relaxed, orbital-optimized VQE—which the supplement names as future work—could shift the binding energies; a sensitivity test using different DFT functionals for orbital selection would show how robust the numbers are.
The binding energies are obtained by subtracting separately computed fragment energies, so if the active spaces differ between complex and isolated fragments, the energy differences carry a systematic bias; recomputing with a consistently defined active space, or using energy embedding, could quantify this effect.
The reported resource estimates—roughly 300–400 two-qubit gates and over 600 Pauli terms—suggest a concrete near-term hardware target: running these same coronene-metal calculations on actual noisy devices rather than simulators.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Worth a look for the benzene data, but the central claim needs a serious reset. The paper combines MCSCF embedding with VQE and computes binding energies for water and Fe/Co/Ni on benzene and coronene. The benzene part is the real content: they compare against CCSD and get errors of 0.11–0.19 eV. That's not chemical accuracy in the usual 0.043 eV sense, but it's a big improvement over the ~1 eV overbinding from GGA, and the ordering of metal binding is recovered. The water dissociation parameter scan (ansatz, optimizer, active space, error mitigation) is thorough and reproducible. The SI is honest that the active-space orbitals are not fully relaxed and that classical FCI is tractable for these spaces. Good credit where due.

The problem is the abstract's claim of chemically accurate predictions for larger strongly correlated systems. On benzene, the errors are a factor of 2–4 above chemical accuracy. On coronene, there is no high-level reference at all. The active space is 6 orbitals, 8 electrons chosen from DFT density/natural orbital analyses, and the SI explicitly says such unoptimized orbitals may be suboptimal for multireference systems. The binding energy decomposition E_M:graphene - E_M - E_graphene requires consistent active spaces and orbital sets across fragments; the paper never states what was used for isolated metal and coronene. If the fragment calculations use different active spaces, the energy differences contain uncontrolled correlation offsets. The stress-test note is on target here. The entire VQE is also simulator-only, so the NISQ-era framing is peripheral.

Who gets value from this? Someone working on embedding-plus-VQE workflows might want the benzene benchmark as a sanity check. The coronene numbers should not be taken as ground truth. The paper is a decent methods development with an overextended conclusion. It deserves a serious referee, but the referee should demand either a high-level benchmark on coronene (CASSCF, DMRG, or at least a larger active space) or a clear specification and consistency check of fragment active spaces, and the abstract should be toned down. If I were editor, I would send it to review with a request for major revision rather than desk reject it—the benzene work is useful enough. Reading group: maybe, for a discussion of overclaims in VQE embedding.

Referee Report

4 major / 5 minor

Summary. The paper presents a hybrid quantum-classical workflow (termed MCSCF-VQE) for computing binding energies of adsorbates on graphene analogues. It validates the pipeline on water dissociation, benchmarks water and Fe/Co/Ni adsorption on benzene against CCSD, and then applies the method to pristine and single-vacancy coronene. The central claim is that the framework achieves chemically accurate predictions for larger strongly correlated systems such as metal–graphene complexes, resolving charge-transfer and multireference effects that DFT misrepresents.

Significance. If the claims were fully supported, the work would be a useful step toward practical NISQ-era simulations of correlated adsorbate–surface systems. The benzene benchmark provides a valuable point of comparison, and the authors are transparent in the Supplementary that their orbitals are not CASSCF-optimized. However, the evidence does not currently support the abstract's 'chemically accurate' claim for metal–graphene complexes: the metal–benzene errors versus CCSD are 0.106–0.194 eV, well above the conventional 1 kcal/mol (0.043 eV) threshold, and the coronene predictions have no high-level reference. The methodological details needed to assess the binding-energy decomposition are also missing. The work is therefore more a promising prototype than a validated accuracy claim.

major comments (4)

[Abstract; Table 1] The abstract and conclusion claim 'chemically accurate predictions' for metal–graphene complexes. Table 1 shows MCSCF-VQE errors versus CCSD of 0.106 eV (Fe), 0.115 eV (Co), and 0.194 eV (Ni), all exceeding the standard 1 kcal/mol = 0.043 eV chemical-accuracy threshold by factors of 2.5–4.5. The text itself only claims that MCSCF-VQE 'tracks' CCSD, not that it is chemically accurate. This overclaim must be corrected, or the authors must explicitly define a different accuracy target and justify it.
[Coronene results; Supplementary 'Optimization using CASSCF, and ADAPT-VQE'] For coronene and vacancy-coronene, Table 2 reports MCSCF-VQE binding energies without any high-level reference. The active space is a fixed 6-orbital/8-electron space with orbitals taken from DFT/HF and not relaxed; the Supplementary explicitly states these orbitals 'may be suboptimal for multireference systems.' No active-space convergence study, no comparison to an alternative multireference method (e.g., the pyrene-SV model of ref. 41), and no sensitivity analysis is provided. The DFT comparison is also uncontrolled because DFT uses VASP/PBE plane-wave calculations while MCSCF-VQE uses PySCF/STO-6G on presumably the same geometries. These coronene numbers therefore cannot support the 'chemically accurate' claim.
[Eq. (3); Methods] The binding energy is defined as E_B = E_{M:gr} - E_M - E_gr. This is valid only if the three energies are computed with mutually consistent active spaces, orbital sets, and fragment definitions. The paper specifies the active space for the complex (metal 3d/4s and proximal carbon 2p) but never states the active space used for isolated metal atoms or for the free coronene/graphene fragment, nor whether fragment orbitals are taken from the complex or optimized separately. If these differ, the energy differences contain uncontrolled correlation and basis-set offsets. This affects every reported binding energy, including the benzene benchmark, and must be documented and tested.
[Title; Introduction; Methods] The method is called MCSCF-VQE, but the orbitals are not self-consistently optimized; the workflow uses fixed mean-field orbitals with a CASCI-style active-space Hamiltonian. True MCSCF/CASSCF orbital relaxation is a central ingredient for strongly correlated charge-transfer systems and is precisely what the Supplementary admits is missing. The naming is therefore misleading. Either implement orbital optimization (e.g., the orbital-adapted VQE approaches cited in the Supplementary) or rename the method (e.g., 'fixed-orbital CASCI-VQE') and temper the claims accordingly.

minor comments (5)

[Equation numbering] Equation (3) is used for both a two-electron integral and the binding-energy expression, causing confusion. Renumber the binding-energy equation.
[Table 1; Figure 4] The reported standard deviation is only for water (0.0384 eV). The stochastic VQE runs for Fe, Co, and Ni should also report run-to-run uncertainties, especially since Ni shows a 0.194 eV deviation from CCSD.
[Figures 2 and SI1] The main text selects COBYLA as the preferred optimizer, while SI Figure SI1 states SLSQP converges best in noiseless simulations. Clarify whether the choice is based on robustness to noise, wall-clock time, or accuracy, and present consistent criteria.
[Methods: Active-space selection] The text mentions 'density difference and natural orbital analyses' but does not describe how these were performed, which orbitals were selected, or the occupation thresholds. This information is essential for reproducibility.
[Data Availability] The data availability statement says data are in the article and supplementary material, but no scripts or Hamiltonian/integral files are provided. Depositing the active-space Hamiltonians and VQE parameters would strengthen reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MCSCF-VQE energies are variational solutions of fixed active-space Hamiltonians, and the CCSD comparison is an independent benchmark rather than a fitting target.

full rationale

Central derivation: binding energies are computed as E_B = E_M:gr - E_M - E_gr (Eq. 3 in Results), each term obtained by VQE minimization of the second-quantized active-space Hamiltonian (Eqs. 1-7). The VQE energy is a variational upper bound; no parameter is fitted to the CCSD values in Table 1 or to any target binding energy. The active spaces (CAS(8e,6o) for water; 6 spatial orbitals for metals) are selected via density-difference and natural-orbital analyses (Results: 'Benchmarking on benzene'; Methods), not by tuning to benchmarks. Thus the benzene comparisons are an independent check of the solver and the active-space model, not a circular fit. Water-dissociation tests tune ansatz and optimizer, but those settings are not fitted to benzene/coronene binding energies. No load-bearing self-citation was found: refs 28 and 41 are contextual, and refs 58-60 are cited only as future directions. Flagged limitation (Supplementary, 'Optimization using CASSCF, and ADAPT-VQE'): 'orbitals for the active space are taken from single-determinant DFT or HF and are not fully relaxed, as in true CASSCF... may be suboptimal for multireference systems.' This is a real accuracy threat and makes the 'MCSCF' label misleading, but it is not circular, because orbital choice is not conditioned on reproducing target energies. Flagged missing detail: the active spaces used for isolated monomers in Eq. (3) are not specified; if they differ from the complex active space, the energy differences contain uncontrolled offsets. This threatens the coronene numbers, but no evidence shows those fragment energies were constructed to force the quoted binding energies. Overall, the coronene claim is unsupported by a high-level reference and rests on an unvalidated, non-orbital-optimized active space; that is an extrapolation/validity problem, not a circular reduction.

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

The central claim rests on the chosen active space and the fragmentation approximation; neither is benchmarked for the headline coronene systems, and the fragment active-space consistency is not described.

free parameters (3)

Active space size for metal-coronene = 6 spatial orbitals, 8 electrons
Chosen by hand based on density difference and natural orbital analyses; not systematically benchmarked for coronene or checked for consistency between complex and fragment calculations.
Basis set = STO-6G
Minimal basis set selected for computational feasibility; its limitations for transition-metal bonding are not discussed.
DFT geometry parameters = PBE, 2x2x2 k-grid, 500 eV cutoff, 28x28x28 A box
Standard settings but not validated against experiment or higher-level methods; optimized geometries are used for all subsequent single-point energy methods.

assumptions (4)

standard math Born-Oppenheimer approximation separates nuclear and electronic motion
Invoked implicitly in all electronic structure calculations in the paper.
domain assumption The active space of 6 spatial orbitals and 8 electrons captures the correlation and charge-transfer physics relevant to adsorption
See Methods, active-space selection; this is the core assumption of the embedding and is never validated against a high-level benchmark for coronene.
ad hoc to paper Binding energies can be computed as differences of separate energy calculations, each with its own active space
Equation (3) in the text uses E_B = E_complex - E_metal - E_substrate, but the paper does not specify how active spaces are matched between the complex and the isolated fragments, which can introduce systematic errors.
domain assumption DFT-optimized geometries are adequate for single-point CCSD and MCSCF-VQE calculations
Geometries come from VASP PBE optimizations and are not re-optimized at the higher levels of theory.

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Cite this review

Pith. "Pith review of Hybrid Quantum-Classical Simulations of Graphene Analogues: Adsorption Energetics Beyond DFT." pith.science (2026). https://pith.science/paper/DTKXONUH

@misc{pith2026250821325,
  author       = {Pith},
  title        = {Pith review of: Hybrid Quantum-Classical Simulations of Graphene Analogues: Adsorption Energetics Beyond DFT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DTKXONUH}},
  note         = {Machine review of arXiv:2508.21325}
}

read the original abstract

Understanding strongly correlated systems is essential for advancing quantum chemistry and materials science, yet conventional methods like Density Functional Theory (DFT) often fail to capture their complex electronic behavior. To address these limitations, we develop a hybrid quantum-classical framework that integrates Multiconfigurational Self Consistent Field (MCSCF) with the Variational Quantum Eigensolver (VQE). Our initial benchmarks on water dissociation enabled the systematic optimization of key computational parameters, including ansatz selection, active space construction, and error mitigation. Building on this, we extend our approach to investigate the interactions between graphene analogues and water, demonstrating that our framework produces binding energies consistent with high accuracy quantum methods. Furthermore, we apply this methodology to predict the binding energies of transition metals (Fe, Co, Ni) on both pristine and defective graphene analogues, revealing strong charge transfer effects and pronounced multireference character phenomena often misrepresented by standard DFT. In contrast to many existing quantum algorithms that are constrained to small molecular systems, our framework achieves chemically accurate predictions for larger, strongly correlated systems such as metal graphene complexes. This advancement highlights the capacity of hybrid quantum-classical approaches to address complex electronic interactions and demonstrates a practical route toward realizing quantum advantage for real world materials applications in the Noisy Intermediate Scale Quantum (NISQ) era.

Figures

Figures reproduced from arXiv: 2508.21325 by the authors.

**Figure 1.** Framework of evaluating electronic properties of a strongly correlated material and adsorbent system where electron correlations can be significant. (a). An example of such a system is shown consisting of a transition metal adsorbed on pristine and defective coronene. The environment is evaluated at a mean-field level of theory like HF or DFT. The effective Hamiltonian of the active space is evaluated using a chosen… view at source ↗

**Figure 2.** Potential Energy Surface (PES) of water dissociation using VQE. [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Error mitigation of water dissociation using VQE and MCSCF-VQE. (a) Comparison of the mitigated, unmitigated and ideal PES curves of water dissociation with the Noiseless backend. (b) Comparison of the mitigated, unmitigated and ideal PES curves of water dissociation with the Noiseaugmented backend. Incorporation of noise causes large discrepancies in the predicted PES (unmitigated; green circles), which is improve… view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Water (2-leg orientation) interacting with benzene. (a) The electronic configuration of the active space is shown, along with the orbitals which contribute most significantly to the electron density. Oxygen is shown in red, carbon in teal and hydrogen in white. (b) Bin…

**Figure 5.** Figure 5: Fe, Co, Ni interacting with pristine and single vacancy coronene. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

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Works this paper leans on

17 extracted references · 11 canonical work pages

[1]

Inhomogeneous electron gas

(1) Hohenberg, P.; Kohn, W. Inhomogeneous electron gas. Physical review 1964, 136 (3B), B864. (2) Sherrill, C. D.; Schaefer III, H. F. The configuration interaction method: Advances in highly correlated approaches. In Advances in quantum chemistry, Vol. 34; Elsevier, 1999; pp 143-

work page 1964
[5]

H.; Liu, Y

(19) Kim, I. H.; Liu, Y. -H.; Pallister, S.; Pol, W.; Roberts, S.; Lee, E. Fault -tolerant resource estimate for quantum chemical simulations: Case study on Li -ion battery electrolyte molecules. Physical Review Research 2022, 4 (2), 023019. (20) Ivanov, A. V.; Sünderhauf, C.; Holzmann, N.; Ellaby, T.; Kerber, R. N.; Jones, G.; Camps, J. Quantum computati...

arXiv 2022
[27]

O.; Barkoutsos, P

(43) Sokolov, I. O.; Barkoutsos, P. K.; Ollitrault, P. J.; Greenberg, D.; Rice, J.; Pistoia, M.; Tavernelli, I. Quantum orbital -optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical e quivalents? The Journal of chemical physics 2020, 152 (12). (44) Barkoutsos, P. K.; Gonthier, J. F....

work page 2020
[42]

Theory of fault-tolerant quantum computation

(24) Gottesman, D. Theory of fault-tolerant quantum computation. Physical Review A 1998, 57 (1),

work page 1998
[127]

Chemistry beyond the scale of exact diagonalization on a quantum -centric supercomputer

(25) Robledo -Moreno, J.; Motta, M.; Haas, H.; Javadi -Abhari, A.; Jurcevic, P.; Kirby, W.; Martiel, S.; Sharma, K.; Sharma, S.; Shirakawa, T. Chemistry beyond the scale of exact diagonalization on a quantum -centric supercomputer. Science Advances 2025, 11 (25), eadu9991. (26) Knizia, G.; Chan, G. K. -L. Density matrix embedding: A strong -coupling quant...

work page 2025
[269]

D.; Ma, D.; Olsen, J.; Gagliardi, L.; De Jong, W

(3) Vogiatzis, K. D.; Ma, D.; Olsen, J.; Gagliardi, L.; De Jong, W. A. Pushing configuration - interaction to the limit: Towards massively parallel MCSCF calculations. The Journal of chemical physics 2017, 147 (18). (4) Čížek, J. On the correlation problem in atomic and molecular systems. Calculation of wavefunction components in Ursell ‐type expansion us...

work page 2017
[509]

B.; Kitaev, A

(34) Bravyi, S. B.; Kitaev, A. Y. Fermionic quantum computation. Annals of Physics 2002, 298 (1), 210-226. (35) Verstraete, F.; Cirac, J. I. Mapping local Hamiltonians of fermions to local Hamiltonians of spins. Journal of Statistical Mechanics: Theory and Experiment 2005, 2005 (09), P09012. (36) Seeley, J. T.; Richard, M. J.; Love, P. J. The Bravyi -Kita...

arXiv 2002
[1758]

(55) Blöchl, P. E. Projector augmented-wave method. Physical review B 1994, 50 (24), 17953. (56) Sun, Q.; Zhang, X.; Banerjee, S.; Bao, P.; Barbry, M.; Blunt, N. S.; Bogdanov, N. A.; Booth, G. H.; Chen, J.; Cui, Z.-H. Recent developments in the PySCF program package. The Journal of chemical physics 2020, 153 (2). (57) Sun, Q.; Berkelbach, T. C.; Blunt, N....

arXiv 1994

Show all 17 references

[1988]

(47) Spall, J. C. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins apl technical digest 1998, 19 (4), 482-492. (48) Byrd, R. H.; Lu, P.; Nocedal, J.; Zhu, C. A limited memory algorithm for bound constrained optimization. SIAM Journa...

1998
[1993]

(30) Veldhorst, M.; Yang, C.; Hwang, J.; Huang, W.; Dehollain, J.; Muhonen, J.; Simmons, S.; Laucht, A.; Hudson, F.; Itoh, K. M. A two-qubit logic gate in silicon. Nature 2015, 526 (7573), 410-414. (31) Kaye, P.; Laflamme, R.; Mosca, M. An introduction to quantum computing; OU...

2015
[1995]

(23) Chao, R.; Reichardt, B. W. Fault -tolerant quantum computation with few qubits. npj Quantum Information 2018, 4 (1),

2018
[2006]

-P.; Li, M.; Markov, I

(32) Wang, Q.; Cian, Z. -P.; Li, M.; Markov, I. L.; Nam, Y. Ever more optimized simulations of fermionic systems on a quantum computer. In 2023 60th ACM/IEEE Design Automation Conference (DAC), 2023; IEEE: pp 1-6. (33) Wang, Q.; Li, M.; Monroe, C.; Nam, Y. Resource-optimized f...

2023
[2017]

(38) Setia, K.; Whitfield, J. D. Bravyi-Kitaev Superfast simulation of electronic structure on a quantum computer. The Journal of chemical physics 2018, 148 (16). (39) Steudtner, M.; Wehner, S. Fermion -to-qubit mappings with varying resource requirements for quantum simulatio...

2018 arXiv
[2021]

P.; Jayee, B.; Aquino, A

(41) Nieman, R.; Oliveira, V. P.; Jayee, B.; Aquino, A. J.; Machado, F. B.; Lischka, H. High - Level Multireference Investigations on the Electronic States in Single -Vacancy (SV) Graphene Defects Using a Pyrene-SV Model. The Journal of Physical Chemistry A 2023, 127 (40), 828...

2023
[2024]

S.; Delgado, A.; Voigt, A

(18) Fomichev, S.; Hejazi, K.; Loaiza, I.; Zini, M. S.; Delgado, A.; Voigt, A. -C.; Mueller, J. E.; Arrazola, J. M. Simulating X-ray absorption spectroscopy of battery materials on a quantum computer. arXiv preprint arXiv:2405.11015

arXiv
[3865]

Efficient iterative schemes for ab initio total -energy calculations using a plane-wave basis set

(53) Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total -energy calculations using a plane-wave basis set. Physical review B 1996, 54 (16), 11169. (54) Kresse, G.; Joubert, D. From ultrasoft pseudopotentials to the projector augmented - wave method. Ph...

1996
[4213]

A.; Collaborators*†; Arute, F.; Arya, K.; Babbush, R.; Bacon, D.; Bardin, J

(6) Quantum, G. A.; Collaborators*†; Arute, F.; Arya, K.; Babbush, R.; Bacon, D.; Bardin, J. C.; Barends, R.; Boixo, S.; Broughton, M.; et al. Hartree -Fock on a superconducting qubit quantum computer. Science 2020, 369 (6507), 1084-1089. (7) Kandala, A.; Mezzacapo, A.; Temme,...

2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Inhomogeneous electron gas

(1) Hohenberg, P.; Kohn, W. Inhomogeneous electron gas. Physical review 1964, 136 (3B), B864. (2) Sherrill, C. D.; Schaefer III, H. F. The configuration interaction method: Advances in highly correlated approaches. In Advances in quantum chemistry, Vol. 34; Elsevier, 1999; pp 143-

work page 1964

[2] [5]

H.; Liu, Y

(19) Kim, I. H.; Liu, Y. -H.; Pallister, S.; Pol, W.; Roberts, S.; Lee, E. Fault -tolerant resource estimate for quantum chemical simulations: Case study on Li -ion battery electrolyte molecules. Physical Review Research 2022, 4 (2), 023019. (20) Ivanov, A. V.; Sünderhauf, C.; Holzmann, N.; Ellaby, T.; Kerber, R. N.; Jones, G.; Camps, J. Quantum computati...

arXiv 2022

[3] [27]

O.; Barkoutsos, P

(43) Sokolov, I. O.; Barkoutsos, P. K.; Ollitrault, P. J.; Greenberg, D.; Rice, J.; Pistoia, M.; Tavernelli, I. Quantum orbital -optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical e quivalents? The Journal of chemical physics 2020, 152 (12). (44) Barkoutsos, P. K.; Gonthier, J. F....

work page 2020

[4] [42]

Theory of fault-tolerant quantum computation

(24) Gottesman, D. Theory of fault-tolerant quantum computation. Physical Review A 1998, 57 (1),

work page 1998

[5] [127]

Chemistry beyond the scale of exact diagonalization on a quantum -centric supercomputer

(25) Robledo -Moreno, J.; Motta, M.; Haas, H.; Javadi -Abhari, A.; Jurcevic, P.; Kirby, W.; Martiel, S.; Sharma, K.; Sharma, S.; Shirakawa, T. Chemistry beyond the scale of exact diagonalization on a quantum -centric supercomputer. Science Advances 2025, 11 (25), eadu9991. (26) Knizia, G.; Chan, G. K. -L. Density matrix embedding: A strong -coupling quant...

work page 2025

[6] [269]

D.; Ma, D.; Olsen, J.; Gagliardi, L.; De Jong, W

(3) Vogiatzis, K. D.; Ma, D.; Olsen, J.; Gagliardi, L.; De Jong, W. A. Pushing configuration - interaction to the limit: Towards massively parallel MCSCF calculations. The Journal of chemical physics 2017, 147 (18). (4) Čížek, J. On the correlation problem in atomic and molecular systems. Calculation of wavefunction components in Ursell ‐type expansion us...

work page 2017

[7] [509]

B.; Kitaev, A

(34) Bravyi, S. B.; Kitaev, A. Y. Fermionic quantum computation. Annals of Physics 2002, 298 (1), 210-226. (35) Verstraete, F.; Cirac, J. I. Mapping local Hamiltonians of fermions to local Hamiltonians of spins. Journal of Statistical Mechanics: Theory and Experiment 2005, 2005 (09), P09012. (36) Seeley, J. T.; Richard, M. J.; Love, P. J. The Bravyi -Kita...

arXiv 2002

[8] [1758]

(55) Blöchl, P. E. Projector augmented-wave method. Physical review B 1994, 50 (24), 17953. (56) Sun, Q.; Zhang, X.; Banerjee, S.; Bao, P.; Barbry, M.; Blunt, N. S.; Bogdanov, N. A.; Booth, G. H.; Chen, J.; Cui, Z.-H. Recent developments in the PySCF program package. The Journal of chemical physics 2020, 153 (2). (57) Sun, Q.; Berkelbach, T. C.; Blunt, N....

arXiv 1994