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.
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.
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arXiv AI
Sep 7
Qlippy: A Retrieval-Augmented GenAI Assistant for Reproducible Quantum Workflows and Experiment Tracking
Qlippy is a retrieval‑augmented generative AI assistant designed to support reproducible quantum software development. It is embedded in the development environment and grounds its responses in a curated corpus of quantum‑software‑engineering knowledge, explaining reproducibility and provenance concepts in context. The assistant augments Qiskit programs with MLflow‑based experiment tracking that follows the QProv schema, thereby reducing reliance on large language models and enabling low‑cost, privacy‑preserving local deployment.
arXiv AI
Jul 13
QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming
arXiv:2508. 20134v2 Announce Type: replace Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration.
arXiv Machine Learning
5d ago
QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
arXiv:2605. 30358v2 Announce Type: replace Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, with performance constrained by noise.
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.
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
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
Jun 26
Efficient foundation decoders for fault-tolerant quantum computing
arXiv:2606. 27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code distances.
arXiv Machine Learning
Sep 18
QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?
QEncodeBench evaluates whether large language models can translate classical constraint problems into verified quantum phase oracles. The benchmark measures the correctness of generated circuits using an adversarial self‑validated verifier that checks full solution‑set equivalence while enforcing resource limits. Results show that models lacking a reasoning mode perform poorly, whereas enabling native reasoning improves accuracy tenfold; semantic errors dominate, and neuro‑symbolic pipelines close most gaps by delegating critical composition to deterministic procedures.
arXiv AI
Aug 14
AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization
arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
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.
Hugging Face Trending Papers
Aug 13
AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization
As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability.