arXiv AI
Jul 29
OmniQEC: discovering practical quantum error-correcting codes by an AI scientist
arXiv:2607. 25865v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing.
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.
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.
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.
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.
arXiv AI
Jun 2
Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search
arXiv:2606. 02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any candidates found.
arXiv AI
Aug 11
Multi-agent discovery of practical quantum LDPC codes
arXiv:2608. 08996v1 Announce Type: cross Abstract: Quantum low-density parity-check (qLDPC) codes can encode multiple logical qubits using sparse parity checks, yet searching for useful finite-length instances remains a challenging design problem because code performance must be optimized while satisfying practical constraints.
arXiv AI
Jun 24
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
arXiv:2606. 24808v1 Announce Type: cross Abstract: Quantum computers could outperform classical machines on important problems, but only if the errors that pervade quantum hardware can be corrected at scale.
arXiv AI
Jul 24
PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation
arXiv:2605. 25572v2 Announce Type: replace-cross Abstract: The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based code assistants: general-purpose models hallucinate PennyLane-specific gate names, misplace device configurations, and produce structurally invalid circuits when faced with specialized quantum coding challenges.
arXiv AI
Jun 17
From Paper to Program: Knowledge Externalization for AI-Assisted Quantum Many-Body Code Generation
arXiv:2604. 04089v3 Announce Type: replace-cross Abstract: Large language models can write scientific code, but direct paper-to-program translation remains fragile when correctness depends on tacit conventions in the literature.
arXiv AI
Jul 29
KQFuzz: Knowledge-Guided Fuzzing for Quantum Libraries via Large Language Models
arXiv:2607. 25647v1 Announce Type: cross Abstract: As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical.