Answer-Free Configuration Recovery for Sample-Based Quantum Diagonalization at Deep Circuit Depths

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-15
DOI
https://doi.org/10.5281/zenodo.22767522
Primary Topic
Quantum Computing Algorithms and Architecture
Type
preprint
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preprint

Answer-Free Configuration Recovery for Sample-Based Quantum Diagonalization at Deep Circuit Depths

Takeshi Okuda
Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
preprint

Answer-Free Configuration Recovery for Sample-Based Quantum Diagonalization at Deep Circuit Depths

Takeshi Okuda
preprint en

Abstract

A previous report (Okuda, Zenodo DOI: 10.5281/zenodo.22469203) identified a discrete failure mode in sample-based quantum diagonalization (SQD): at low shot counts, classical configuration recovery can lock onto a reproducible but incorrect "wrong attractor," and injecting a single correct configuration into the raw sample can trigger a discrete escape from it. That finding was established on a shallow circuit (N2, CAS(6,6), 166 two-qubit gates) where the wrong-attractor structure itself was directly observable. Here we address two questions left open by that work. First, was the original demonstration merely a sample-size artifact rather than evidence that the specific injected configuration mattered? We answer this with a controlled random-injection experiment (Fe2S2, CAS(12,12), 2053 two-qubit gates, 76-80 independent trials per condition): recovery probability rises from 14.5% with no injection to 100.0% with one pre-specified escape configuration, while a random single-configuration injection of the same size produces only 15.0% recovery, and the escape itself is discrete while the residual energy error continues to improve smoothly with further injected configurations. Second, at circuit depths beyond roughly 1000 two-qubit gates, the wrong-attractor structure itself often becomes too noise-dominated to observe directly, so a practical recovery procedure cannot simply look for and inject "the" known wrong-attractor escape configuration. We present such a procedure: a small set of chemically motivated candidate configurations is generated and injected, and the best candidate is selected using one of three answer-free criteria (variational energy, escape rate from a known baseline attractor, or seed-to-seed energy variance), chosen by a cheap upfront classification of the target system. Across 23 independent hardware-data trials spanning two IBM Quantum backends and 21 distinct molecular systems and active-space combinations, this decision rule selected the correct candidate in all 23 trials, including one case improving accuracy by approximately 1000x. We report this as validation of the decision framework across a broad range of hardware and chemical conditions, not as evidence of answer-free prediction for arbitrary, genuinely unknown multi-reference chemistry, and report the framework's limitations candidly. Open-source implementation: https://github.com/okudat9/escape-config-predictor

Zenodo (CERN European Organization for Nuclear Research)
Quantum Computing Algorithms and Architecture
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