QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

Title:QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

Abstract:Grover search, amplitude amplification, and quantum counting all rely on the same reusable subroutine, a phase oracle, whose construction the algorithms literature takes as given: the classical predicate is assumed to be already encoded as a correct, resource-bounded circuit. We turn this assumption into a measured capability. QEncodeBench tasks large language models (LLMs) with encoding classical constraint problems as phase oracles and scores the generated circuits with an adversarially self-validated verifier that decides full solution-set equivalence up to a global phase, with ancillas restored and resource budgets enforced. Sampled basis-state tests, we show, systematically overestimate this ability. Measured this way, models separate sharply: code models without a reasoning mode solve essentially nothing, and enabling native reasoning on identical weights improves accuracy by an order of magnitude. The failures are overwhelmingly semantic rather than syntactic. Two architectures, a unit-verified constraint agent and a neuro-symbolic compilation pipeline, close most of the remaining gap by delegating correctness-critical composition to deterministic procedures. Ablations quantify the contribution of each component, and resource gating exposes an architecture-dependent trade-off between circuit width and depth. Finally, controlled difficulty escalation reveals architecture-specific responses to difficulty structure: different difficulty axes degrade different methods, while the neuro-symbolic pipeline passes every evaluated instance. Code and data are available at this https URL.

Quantum Physics (quant-ph); Machine Learning (cs.LG)

arXiv:2609.20319 [quant-ph]

(or arXiv:2609.20319v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.20319 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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