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REVIEW 3 major objections 7 minor 1 cited by

Quantum Computing in Corrosion Modeling: Bridging Research and Industry

T0 review · 3 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Modeling one corrosion step on a quantum computer needs roughly 135,000 physical qubits and about two hours for a six-orbital active space, while classical exact diagonalization of the same Hamiltonian takes milliseconds.

desk verdict An honest, well-executed negative resource-estimation study for quantum chemistry on a corrosion-relevant reaction, but the chemical motivation rests on an active-space artifact that needs independent validation. read the letter →

arxiv 2412.07933 v2 pith:NLVDEZPM submitted 2024-12-10 quant-ph physics.chem-ph

Juan Manuel Aguiar Hualde , Marek Kowalik , Lian Remme , Franziska Elisabeth Wolff , Julian van Velzen , Walden Killick , Rene Bottcher , Christian Weimer
show 2 more authors
Jasper Krauser Emanuele Marsili
This is my paper · ORCID
classification quant-phphysics.chem-ph
keywords corrosionmodelingoxygenreductionreactionquantumresourceestimationvariationaleigensolverphaseactivespaceembeddingsurfacecodeoverheadaerospacealuminumalloys
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 establish where quantum computing could and could not help industrial corrosion modeling. It builds a classical workflow—DFT, Hartree-Fock, and full configuration interaction—for the reductive dissociation of oxygen on a copper surface, identifies the geometries whose ground states have strong multiconfigurational character, and then asks what it would cost to solve those small electronic-structure problems with variational quantum eigensolver and quantum phase estimation algorithms. Its resource estimates say the cost is prohibitive: a (10/6) active space requires roughly 135,000 physical qubits and about two hours for QPE, while VQE with error correction exceeds one hour per iteration; exact diagonalization of the same Hamiltonian is done classically in milliseconds. The conclusion a sympathetic reader should take is that quantum computing will not contribute to corrosion simulation without major algorithmic and error-correction breakthroughs, and the paper also fixes a concrete benchmark problem and workflow for testing those advances.

What carries the argument

The machinery is the embedded active-space Hamiltonian: automated atomic-valence active-space selection picks the oxygen 2p and copper 3d orbitals, Hartree-Fock provides the reference, and the remaining electrons are folded into effective one-body interactions. The qubit form is obtained by Jordan-Wigner mapping, and the two quantum algorithms are UCCSD-based VQE and Trotterized QPE. The resource counts come from surface-code quantum error correction with an assumed physical error rate of $10^{-4}$ and all-to-all connectivity, whose dominant term is the overhead of T factories—the blocks that produce magic states needed for fault-tolerant T gates.

What would settle it

Run DMRG or CASPT2 on the same five geometries with an active space that includes at least one additional virtual orbital beyond the sigma-star orbital; if the ground state becomes single-reference dominant and the correlation-energy difference at the dissociation transition state shrinks to noise, the paper's identification of a strongly correlated target is falsified.

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Extended reading notes

Core claim

The central discovery is a negative-resource result embedded in a positive workflow. For the reductive dissociation step of the oxygen reduction reaction on a copper surface, the authors construct embedding Hamiltonians for three active spaces, all containing only one virtual orbital—the sigma-star O-O orbital—so the FCI expansion stops at double excitations. They find that the reactant and transition-state geometries have a strong doubly excited configuration in the ground state, which is what makes the problem a candidate for quantum simulation. They then estimate the cost of solving the same eigenproblem with VQE and QPE under surface-code error correction: the (10/6) active space requires about 135,000 physical qubits, mostly in T factories, and about two hours of QPE runtime, while VQE with quantum error correction exceeds one hour per iteration. Because the same Hamiltonian is diagonalized exactly on a laptop-class calculation in milliseconds, the paper's argued conclusion is that quantum advantage is not in reach for this problem class, and that hardware improvements alone will not close the gap.

Load-bearing premise

The entire case for a quantum-computing target rests on the claim that the reactant and transition-state geometries are genuinely multiconfigurational, which is established only inside active spaces containing a single virtual orbital—so a larger active space or a multireference benchmark could dissolve the motivation.

Editorial extensions

If this is right

  • If correct, even a small six-orbital corrosion-relevant Hamiltonian is hours away from being solved on a fault-tolerant quantum computer, so realistic models with dozens of active orbitals are far beyond any near-term device.
  • VQE's dominant cost is the shot count needed to sample Hamiltonian terms to chemical accuracy; with surface-code error correction this pushes per-iteration runtime past one hour for the (10/6) active space.
  • QPE's runtime is set by Trotter steps and ancilla qubits, and its physical-qubit count is dominated by T factories, reaching about 135,000 qubits for the largest active space tested.
  • For both algorithms, improving physical qubit error rates reduces qubit count but does not substantially reduce runtime, so algorithm and error-correction design, not just hardware, are the bottleneck.
  • The workflow supplies a ready-made benchmark: a set of geometries, active spaces, and Hamiltonians whose classical ground states are known, on which future quantum algorithms can be tested.

Reading between the lines

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

  • Beyond the paper's explicit claims, a natural next test is to run a higher-level multireference benchmark such as DMRG or CASPT2 with a larger active space that includes additional virtual orbitals; if the multiconfigurational weight of the ground state falls sharply, the case for quantum simulation of this reaction becomes weaker than the paper's active-space choice implies.
  • The paper assumes, rather than demonstrates, that reductive dissociation is the rate-limiting step of the oxygen reduction reaction; a microkinetic or experimental comparison of the three electron-transfer steps would determine whether this bottleneck premise holds.
  • If the resource estimates are correct, the near-term route for this problem class is algorithmic, not hardware-only: the paper's own survey of qubitization, quantum walks, double factorization, and improved error correction suggests those ideas must be brought to bear before the predicted hour-scale runtimes can shrink.
  • One could test the shot-count model directly by measuring the Hamiltonian coefficient distribution on a real device; the paper's observation that large coefficients become relatively less numerous as the active space grows implies that larger spaces may not suffer from shot noise as severely as naive term counting predicts.
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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

3 major / 7 minor

Summary. This paper develops and demonstrates a hybrid classical-quantum workflow for the initial steps of the oxygen reduction reaction (ORR) on a copper surface, used as a model for Cu-rich intermetallic particles in aerospace aluminum alloys. The authors combine DFT (PBE), Hartree-Fock, and FCI in AVAS-constructed active spaces of (2/2), (6/4), and (10/6) electrons/orbitals to map the reductive dissociation pathway, and they report multiconfigurational character near the reactant and at the dissociation transition state. Using the resulting embedding Hamiltonians, they run VQE (UCCSD) simulations and fault-tolerant QPE resource estimations with the Microsoft Azure Quantum Resource Estimator, concluding that even these small active spaces demand prohibitively large resources: roughly two hours and about 135,000 physical qubits for QPE on the (10/6) active space, and over one hour per VQE iteration with error correction. The paper is explicitly negative in its central conclusion, and it discloses several limitations, including the use of classical diagonalization to set the QPE parameters and the restriction of all active spaces to a single virtual orbital.

Significance. If the chemical premise holds, this is a valuable and unusually honest feasibility study: it is, to my knowledge, the first corrosion-specific workflow that carries a concrete molecular problem from DFT/NEB through embedding, VQE, and QPE to physical resource counts, using reproducible standard toolchains (Quantum ESPRESSO, PySCF, Qiskit, Azure Quantum Resource Estimator). The resource estimates rest on explicitly stated physical assumptions (gate times, error rates, Trotter steps, ancilla counts) and on a careful shot-count derivation in the supplementary information whose worst-case bound is parameter-free apart from the chemical-accuracy threshold. The paper deserves explicit credit for flagging the circular use of the classically computed spectrum in the QPE parameter choice (SI Sec. 1: "defeats the purpose") and for disclosing that every active space contains exactly one virtual orbital (Sec. 2.2.1).

major comments (3)
  1. [Sec. 2.2.1, Fig. 3] The central chemical finding, namely strong multiconfigurational character at the reactant and TSdiss geometries, is supported only by FCI calculations in which every active space contains a single virtual orbital (sigma* O-O). As the authors themselves note, this limits excitations to doubles, so the large FCI-HF correlation energies in Fig. 3C and the leading double-excitation coefficients in Fig. 3D measure the response of a deliberately truncated Hilbert space rather than a robustness property of the embedded O2/Cu Hamiltonian. This is load-bearing for the paper's motivation, and I therefore ask for a robustness test: enlarge at least the (10/6) space with additional virtual orbitals, or benchmark with a multireference method including dynamical correlation (CASPT2/NEVPT2 or DMRG). The reversed thermodynamic driving force in the (6/4)/(10/6) FCI profiles of Fig. 3B should also be revisited in that test, since it currently contradicts the DFT profile and may be an artifact of the missing correlation space.
  2. [Sec. 2.3.2, Methods, SI Sec. 1] The QPE resource numbers are conditioned on parameters chosen with knowledge of the exact eigenspectrum obtained by classical diagonalization, which SI Sec. 1 explicitly concedes "defeats the purpose of using the QPE algorithm." This matters quantitatively because the QPE runtime scales exponentially with the number of ancilla qubits (the circuit contains on the order of 2^m controlled evolutions): if the (10/6) estimate required 16 instead of 15 ancilla qubits, or 14 instead of 13 for (6/4), the reported runtimes would roughly double or quadruple. The convergence analysis in the SI covers only the (2/2) and (6/4) Hamiltonians, and for (6/4) the claimed-sufficient 13 ancilla setting was not actually simulated (the text reports only that 12 ancilla "fall very close" to the chemical-accuracy band); no convergence data are given for (10/6). The authors should provide the (10/6) convergence data or an explicit extrapolation procedure, reconcile the (2/2) inconsistency described in the minor comments, and add a sensitivity analysis of runtime to the ancilla and Trotter-step choices. This does not overturn the qualitative impracticality conclusion, but it is load-bearing for the specific headline numbers (about two hours, 135,000 qubits).
  3. [Sec. 2, first paragraph] The paper identifies the reductive dissociation (step 2) as the bottleneck of the overall ORR and justifies the choice of this reaction step by "anticipat[ing]" it to be rate-determining and "speculat[ing]" about strong correlation. The competing steps, in particular the first electron transfer, are not computed, so the bottleneck claim is asserted rather than demonstrated. Because the industrial-relevance framing of the paper rests on this step being the controlling reaction, I ask the authors either to compute or cite barriers for the competing ORR steps, or to reformulate the bottleneck language so that the selection of step 2 is presented as a motivated model choice rather than an established mechanistic conclusion.
minor comments (7)
  1. [Sec. 2.3.2] The sentence "the QPE algorithm provides only a polynomial advantage with respect to classical methods – due to the exponential reduction of the overlap between the HF wavefunction and the true ground state" is internally inconsistent: an exponentially small HF overlap implies an exponential QPE runtime starting from the HF state, not a polynomial advantage. Please rephrase using the argument of reference [35].
  2. [Methods vs. SI Figs. SI2-SI3] The Methods section states that 3 and 1 Trotter steps with 10 and 13 ancilla qubits were sufficient for (2/2) and (6/4), respectively, but the SI text for (2/2) says that for n=3 and n=4 the results with 10 and 12 ancilla qubits remain outside the chemical-accuracy band, and for (6/4) it acknowledges that the 13-ancilla point was not simulated. These statements should be reconciled.
  3. [SI Sec. 1, text near Fig. SI1] The sentence "Fig. SI 1 demonstrates that the choice of the number of Trotter steps, n, is a critical parameter" appears to refer to Fig. SI2, not the eigendecomposition figure; please correct the cross-reference.
  4. [Sec. 2.1 and Abstract] There are typographical errors that should be fixed: "his reaction is catalyzed" should be "this reaction is catalyzed" in Sec. 2.1, and the abstract's reaction equation "O 2 + 4 e- 2 O2-" is garbled.
  5. [Fig. 4A caption] The caption reads "Empty stars are the estimated denote calculations without considering the overhead of QEC"; the wording is incomplete and should be rewritten.
  6. [Supplementary references] The SI reference list duplicates and renumbers main-text references (for example, SI ref. [7] is the same Qiskit paper as main-text ref. [48]); if the SI is meant to be read standalone, the numbering should be made consistent.
  7. [Data availability] Given the emphasis on resource estimation, the authors should consider providing the embedding Hamiltonian matrix elements and the Azure Quantum Resource Estimator input parameters as supplementary machine-readable files, rather than only "upon request," to make the runtime and qubit counts fully reproducible.

Circularity Check

1 steps flagged · score 2.0 of 10

QPE resource estimates use parameters fitted to the exact classical eigenspectrum, but the paper's central qualitative conclusion is independent; overall circularity is minor.

  1. other [Supplementary Information, Section 1 (Convergence of quantum phase estimation); Methods, Section 4 (Quantum Computing).]
    "The Hamiltonian’s exact eigenspectrum can be obtained by diagonalizing the Hamiltonian. Although this implies solving the problem classically (which defeats the purpose of using the QPE algorithm), it allows us to extract valuable information. Specifically, ϵ = 0.8 a.u. and ϵ = 3.91 a.u. were used ... These values were selected somewhat arbitrarily to be slightly higher than the modulus of the largest eigenvalue of the two operators ... we determined that 3 and 1 Trotter steps, along with 10 and 13 ancilla qubits, were sufficient to achieve chemical accuracy."

    The QPE resource estimates (runtime, ancilla qubits, algorithmic qubits, T-factory counts) are functions of the Trotter-step count, the phase-scaling factor ε, and the number of ancilla qubits. Those parameters were chosen by comparing simulated QPE eigenvalues against the exact eigenvalues obtained from classical diagonalization of the same Hamiltonian. The resource estimate is therefore not an independent first-principles prediction: it is the cost of a circuit designed using the exact answer, as the paper itself concedes when it says this 'defeats the purpose' of QPE.

full rationale

The paper's derivation chain is mostly self-contained. The DFT/NEB geometries, HF/FCI energy profiles, and VQE validation against exact diagonalization are standard calculations that do not reduce to the paper's conclusions. The main circular ingredient is in the QPE resource estimate: the scaling factor ε, Trotter-step counts, and ancilla-qubit counts are selected by comparing QPE simulations against the exact eigenspectrum from classical diagonalization, which the authors describe as 'defeating the purpose' of QPE. The runtime and physical-qubit numbers in Fig. 4C therefore inherit these fitted parameters and are not fully first-principles predictions. This is a genuine but localized circularity; it affects the precise resource numbers, not the central qualitative conclusion, because underestimating ε or the Trotter-step count would only make the estimated resources larger. The characterization of the active spaces as strongly correlated is also conditioned on the explicitly disclosed choice of a single virtual orbital (σ* O–O); that is a limitation of the chemical motivation rather than a circular derivation. There are no load-bearing self-citations, uniqueness arguments, or ansatz-smuggling steps. Overall circularity is minor and non-central, so the score is 2.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The ledger highlights algorithmic choices (epsilon, Trotter steps) that are tuned using classical knowledge of the Hamiltonian, and the domain assumptions needed for the chemistry model. No new physical entities are introduced.

free parameters (4)
  • QPE scaling factor epsilon = 0.8 a.u. (2/2), 3.91 a.u. (6/4)
    Chosen somewhat arbitrarily to map eigenvalues into (0,1] after classical diagonalization of the Hamiltonian; affects phase readout and resource estimates.
  • Trotter steps = 3 (2/2), 1 (6/4)
    Empirically determined by comparing QPE estimates to exact eigenvalues from classical diagonalization; a free algorithmic choice for resource estimation.
  • Active space truncation = (2/2), (6/4), (10/6) with a single virtual orbital
    The active space is built from O 2p and Cu 3d orbitals but includes only one virtual orbital, limiting excitation level; the choice is ad hoc and not benchmarked.
  • Physical qubit error rate and gate times = error rate 1e-4, T gate 50 ns, measurement 100 ns
    Optimistic hardware assumptions from the Azure resource estimator; results depend on these values.
assumptions (5)
  • domain assumption The embedding Hamiltonian formalism of Battaglia et al. correctly reduces the full electronic structure problem to the active space with an effective one-body potential.
    Used in Sec 2.3 to build quantum-ready Hamiltonians; the paper relies on this published method without independent validation in this system.
  • domain assumption DFT (PBE) with a three-layer Cu slab and Gamma-point sampling yields accurate geometries and reaction barriers for the ORR step.
    All geometries and NEB profiles come from this setup; no convergence checks with respect to k-points or slab thickness are reported in the main text.
  • ad hoc to paper The rate-determining step of the ORR is the reductive dissociation (step 2).
    Asserted in Sec 2 as an anticipation; no kinetic constants are computed for steps 1 or 3, so the choice of reaction step is a postulate.
  • domain assumption The AVAS-selected active spaces (O 2p and Cu 3d orbitals) capture the relevant strongly correlated electrons.
    The active space construction in Sec 2.2.2 restricts to these orbitals and one virtual orbital; this determines all correlation results.
  • standard math Jordan-Wigner mapping and the standard quantum algorithms (VQE, QPE) work as assumed.
    The paper uses standard methods; no new quantum algorithmic claim is made.

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Cite this review

Pith. "Pith review of Quantum Computing in Corrosion Modeling: Bridging Research and Industry." pith.science (2026). https://pith.science/paper/NLVDEZPM

@misc{pith2026241207933,
  author       = {Pith},
  title        = {Pith review of: Quantum Computing in Corrosion Modeling: Bridging Research and Industry},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NLVDEZPM}},
  note         = {Machine review of arXiv:2412.07933}
}
read the original abstract

Corrosion presents a major challenge to the longevity and reliability of products across various industries, particularly in the aerospace sector. Corrosion arises from chemical processes occurring on an atomistic scale, which lead to macroscopic degradation. Addressing this issue requires multi-scale modeling approaches, which rely on microscopic parameters that are challenging to measure experimentally or model with conventional quantum chemistry techniques. In this work, we develop and demonstrate a hybrid quantum-classical workflow tailored for atomistic simulations of corrosion processes, with a specific focus on the initial step of the oxygen reduction reaction -- a critical trigger for the corrosion of aluminum alloys widely used in modern aircraft. Using a combination of classical quantum chemistry methods and quantum computing frameworks, we identify reaction geometries characterized by multi-configurational electronic structures that are ideal for exploring with quantum algorithms. For the first time in this context, we explore both noisy intermediate-scale quantum and fault-tolerant quantum algorithms for these multi-configurational system, integrating them within a workflow designed to bridge atomistic simulations with macroscopic modeling approaches, such as finite element methods. Furthermore, we conduct a detailed quantum resource estimation to assess when and how quantum computers may play a meaningful role in tackling these problems. Our results demonstrate that significant advancements in quantum hardware but also in algorithms and error correction techniques are needed to make quantum computation practically viable for this class of problems. Nevertheless, this work establishes a critical foundation for applying quantum computation to corrosion modeling and highlights its potential to address complex, business-relevant challenges in materials science.

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Forward citations

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Reference graph

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