arXiv AI By Jae-Hyun Baek, Byung-Sun Won, Jon-Lark Kim

Researcher-in-the-Loop Evolutionary Discovery of Entanglement-Assisted Quantum Code Families

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arXiv AI
Jun 30

A Machine-Verified Proof of a Quantum-Optimization Conjecture

arXiv:2606. 29687v1 Announce Type: cross Abstract: We report a machine-verified resolution of a problem open for over a decade in quantum optimization: the Farhi, Goldstone and Gutmann (FGG) conjecture that depth-$p$ Quantum Approximate Optimization Algorithm (QAOA) on the ring of disagrees attains approximation ratio $(2p+1)/(2p+2)$ exactly.

By Uri Kol, Maor Ben-Shahar, Kfir Sulimany, Dirk Englund
Hugging Face Trending Papers
Jun 1

Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search

Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found. We introduce an LLM-guided evolutionary workflow in which language models mutate Python programs that generate bivariate-bicycle and perturbed bivariate-bicycle code ansätze.

arXiv AI
Aug 11

QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing

arXiv:2608. 07743v1 Announce Type: new Abstract: Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope.

By Yijing Zuo, Zhe Fu, Zihan Nie, Zhihui Zhu, Haohan Wang
arXiv Machine Learning
Oct 12

QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code

QTestBench is a new framework that evaluates quantum code tests generated by large language models (LLMs) by comparing their verdicts against independent benchmark checkers, rather than treating the tests as ground truth. It prompts models to produce syntactic, semantic, and behavioral tests, injects candidate solutions at runtime, and assesses test quality across three validation cohorts. In benchmarks on PennyLane QHack challenges and Qiskit HumanEval tasks, generated test suites frequently execute but often fail to discriminate correct from faulty implementations, with the best test quality scores of 0.71 and 0.34 respectively.

By Meriem Terki, Nouhaila Innan, Minghao Shao, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique
arXiv AI
Sep 18

Long-horizon autoformalization of a core theorem underlying MIP* = RE

FormalFlow is a system that coordinates AI proving agents under human supervision to tackle long‑horizon formalizations, using a shared blueprint for nested planning, proving, and review loops. The team used it to produce a machine‑checked Lean 4 proof of the quantum soundness of the classical low‑individual‑degree test, a core theorem underlying MIP* = RE, in 63 days. The resulting library contains 126,367 lines of Lean code, all generated by agents, and corrects side conditions while preserving the published error bound under corrected assumptions.

By Sirui Lu, Ruixuan Deng, Yanqiao Zhu, Zhengfeng Ji