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arxiv: 2605.23697 · v1 · pith:6Q5WGRBWnew · submitted 2026-05-22 · 🪐 quant-ph

Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction

Nonia Vaquero-Sabater , Abel Carreras , Lukas Broers , Tomonori Shirakawa , Seiji Yunoki , David Casanova This is my paper

Pith reviewed 2026-05-25 04:06 UTC · model grok-4.3

classification 🪐 quant-ph
keywords QSCIquantum-selected configuration interactionLUCJ ansatzconfiguration recoverysampling noiseN2 dissociationHilbert space explorationhybrid quantum-classical
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The pith

Sampling noise in QSCI with LUCJ, when combined with configuration recovery, improves energies by exploring configurations beyond the ideal ansatz support.

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

The paper studies the interplay of ansatz expressivity, sampling, noise, and recovery in quantum-selected configuration interaction applied to N2 dissociation using the local unitary cluster Jastrow ansatz. Noiseless sampling yields compact but biased configuration sets that restrict the accuracy of the subsequent classical diagonalization, especially in strongly correlated regions. A simple noise model is introduced to show that added randomness generates extra configurations outside the ansatz's reach, and pairing this with recovery produces better energies. Recovery starting from random configurations alone can also build accurate spaces efficiently, underscoring its importance for the method.

Core claim

Noiseless LUCJ sampling in QSCI produces compact and biased configurational spaces that limit the accuracy of the resulting CI energies, particularly in strongly correlated regimes. Introducing a simple noise model shows that sampling noise enhances Hilbert-space exploration by generating additional configurations beyond those supported by the ideal ansatz. When combined with configuration recovery, this leads to systematically improved energies. Recovery alone, starting from randomly generated configurations, can efficiently construct accurate CI spaces.

What carries the argument

The simple noise model applied during LUCJ sampling in QSCI together with the configuration recovery procedure that augments the set of configurations before classical diagonalization.

If this is right

  • Noiseless LUCJ sampling restricts accuracy through biased configuration spaces.
  • Sampling noise adds configurations outside the ansatz support.
  • Noise combined with recovery yields systematically better CI energies.
  • Recovery from random configurations alone builds accurate CI spaces efficiently.

Where Pith is reading between the lines

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

  • Inherent hardware noise could provide a practical advantage in QSCI rather than a drawback.
  • The recovery step may be more decisive for overall performance than the quantum sampling quality.
  • The observed noise benefit might extend to other sampling-based hybrid quantum-classical algorithms.

Load-bearing premise

The simple noise model produces sampling statistics representative of actual quantum hardware errors on the LUCJ ansatz for the chosen N2 active space.

What would settle it

Executing the LUCJ ansatz on real quantum hardware for the N2 system, collecting the sampled configurations, applying recovery, and checking whether the energy improvements match those from the simulated noise model would test the claim.

Figures

Figures reproduced from arXiv: 2605.23697 by Abel Carreras, David Casanova, Lukas Broers, Nonia Vaquero-Sabater, Seiji Yunoki, Tomonori Shirakawa.

Figure 1
Figure 1. Figure 1: (a) QSCI energy profiles for the N2 dissociation obtained by sampling with cc￾pVDZ basis and (10e, 26o). Violet, brown and red lines represent the exact circuit simulation for 1, 2 and truncated-2 (2L’) layers respectively. Navy blue line are the data from the IBM experiment carried out in Ref. 20. (b) Size of the complete CI space generated after the application of the different terms of LUCJ up to L = 2 … view at source ↗
Figure 2
Figure 2. Figure 2: N2 dissociation with cc-pVDZ basis and (10e, 26o). Maroon, gold and green lines represent exact results with different noise levels, from p = 0.2 (yellow) to p = 1 (red). The red line represents ORQA noiseless exact results. Figures (a) and (b) show the energies (up) and energy errors with respect to HCI (down) during the dissociation for configuration recovery iterations 0 and 5 respectively. Before apply… view at source ↗
Figure 3
Figure 3. Figure 3: N2 dissociation with cc-pVDZ basis and (10e, 26o). Maroon, gold and green lines represent the energy obtained using samples drawn from a uniform distribution at different configuration recovery iterations, 0, 1 and 2 respectively. For each distance, the number of configurations generated from the uniform distribution matches the number of configurations obtained from the exact simulation, plus the HF confi… view at source ↗
Figure 4
Figure 4. Figure 4: Energy (a) and size of the generated CI space (b) along the N [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
read the original abstract

Quantum-selected configuration interaction (QSCI) is a promising hybrid quantum-classical approach in which a quantum device generates configurations for subsequent classical diagonalization. Here, we analyze the performance of QSCI combined with the local unitary cluster Jastrow (LUCJ) ansatz, focusing on the interplay between ansatz expressivity, sampling, noise, and configuration recovery. Using the dissociation of N2 in a large active space as a benchmark, we show that noiseless LUCJ sampling produces compact and biased configurational spaces, limiting the accuracy of the resulting CI energies, particularly in strongly correlated regimes. By introducing a simple noise model, we demonstrate that sampling noise can enhance Hilbert-space exploration by generating additional configurations beyond those supported by the ideal ansatz. When combined with configuration recovery, this leads to systematically improved energies. Moreover, recovery alone (starting from randomly generated configurations) can efficiently construct accurate CI spaces, highlighting its central role in QSCI.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript examines Quantum Selected Configuration Interaction (QSCI) with the local unitary cluster Jastrow (LUCJ) ansatz applied to the N2 dissociation in a large active space. It demonstrates that noiseless LUCJ sampling yields compact and biased configuration spaces that limit CI accuracy in strongly correlated regimes. A simple noise model is introduced to show that sampling noise can generate additional configurations, improving Hilbert-space exploration, and when paired with configuration recovery, leads to better energies. Recovery from random configurations is also shown to efficiently build accurate CI spaces.

Significance. If the results hold under hardware-realistic conditions, the work provides concrete numerical evidence on the N2 benchmark that sampling noise can constructively expand the sampled configuration space beyond the ideal LUCJ ansatz expressivity, while highlighting the central role of classical configuration recovery. This offers a potentially useful perspective for near-term hybrid quantum-classical algorithms in quantum chemistry that treat noise as a resource rather than solely a liability.

major comments (2)
  1. [Methods section introducing the simple noise model] The central claim that sampling noise enhances Hilbert-space exploration (and yields improved energies with recovery) rests on the simple noise model generating configuration distributions representative of real-device behavior on the LUCJ ansatz. The manuscript provides no validation or comparison of this model against hardware error characteristics (e.g., correlated gate errors, readout noise, or ansatz-specific decoherence) for the chosen N2 active space; this assumption is load-bearing for transferability of the reported energy gains.
  2. [Results section on N2 benchmark] Table or figure reporting the N2 energies with/without noise and recovery: the quantitative improvement attributed to noise is presented without error bars or statistical analysis over multiple noise realizations, making it difficult to assess whether the observed gains exceed sampling variance in the CI diagonalization step.
minor comments (2)
  1. [Methods] Notation for the LUCJ parameters and the precise definition of the configuration recovery procedure could be clarified with an explicit equation or pseudocode in the methods.
  2. [Abstract] The abstract states outcomes on the N2 benchmark but does not specify the active-space size or the exact form of the noise model; adding these details would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive feedback on our manuscript. We address each major comment below, proposing revisions where the points identify areas for improvement while defending the core contributions on their merits.

read point-by-point responses

  1. Referee: [Methods section introducing the simple noise model] The central claim that sampling noise enhances Hilbert-space exploration (and yields improved energies with recovery) rests on the simple noise model generating configuration distributions representative of real-device behavior on the LUCJ ansatz. The manuscript provides no validation or comparison of this model against hardware error characteristics (e.g., correlated gate errors, readout noise, or ansatz-specific decoherence) for the chosen N2 active space; this assumption is load-bearing for transferability of the reported energy gains.

    Authors: We agree that the simple noise model is an idealized approximation and does not incorporate device-specific error channels such as correlated gate errors or readout noise. Its purpose in the manuscript is to isolate and illustrate the mechanism by which even minimal sampling perturbations can expand the configuration space beyond the ideal LUCJ support, rather than to claim quantitative fidelity to any particular hardware. We will revise the Methods section to explicitly state the model's assumptions and limitations, add a brief discussion of why a minimal model suffices for the conceptual demonstration, and note that hardware validation remains an important direction for future work. This addresses the concern without altering the reported numerical results. revision: partial

  2. Referee: [Results section on N2 benchmark] Table or figure reporting the N2 energies with/without noise and recovery: the quantitative improvement attributed to noise is presented without error bars or statistical analysis over multiple noise realizations, making it difficult to assess whether the observed gains exceed sampling variance in the CI diagonalization step.

    Authors: We acknowledge that the absence of error bars or multi-realization statistics makes it harder to quantify the robustness of the observed energy improvements. We will perform additional independent noise realizations for the reported N2 points, compute standard deviations, and include error bars (or shaded regions) in the revised figures and tables. This will allow readers to assess whether the gains from noise plus recovery exceed the variance arising from both sampling and the subsequent classical CI step. revision: yes

Circularity Check

0 steps flagged

No circularity: numerical benchmark study with independent experimental claims

full rationale

The paper is a numerical benchmark on QSCI + LUCJ for N2 dissociation. It reports simulation outcomes under ideal sampling, a simple noise model, and recovery procedures. No derivation chain, first-principles prediction, or fitted parameter is presented as a result; all claims are direct outputs of the described Monte Carlo sampling and diagonalization experiments. No self-citation is used to justify uniqueness or to close a logical loop. The noise-model assumption is an external modeling choice whose validity can be tested against hardware, not a self-referential definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the simple noise model is an unstated modeling choice whose details are unavailable.

pith-pipeline@v0.9.0 · 5707 in / 1033 out tokens · 22071 ms · 2026-05-25T04:06:40.735708+00:00 · methodology

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