arXiv Machine Learning

Machine-learned syndrome post-selection for reliable quantum error correction

arXiv:2607. 19563v1 Announce Type: cross Abstract: Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-level information.

arXiv AI
Jul 14

MDQEC-QAS: Meta-Decoding for Quantum Error Correction with Hardware-Aware VQC Search and Confidence-Gated Recovery

arXiv:2607. 10707v1 Announce Type: cross Abstract: We propose a unified meta-decoding framework for quantum error correction that learns syndrome-to-recovery mappings across multiple stabilizer codes and noise settings, without requiring separate decoders for each configuration.

By Prashant Kumar Choudhary, Nouhaila Innan, Muhammad Shafique, Rajeev Singh
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.

By Minghao Shao, Nouhaila Innan, Hariharan Janardhanan, Muhammad Kashif, Alberto Marchisio, Muhammad Shafique
arXiv Machine Learning
Aug 7

Provably Efficient Self-Calibrating Quantum Fault Tolerance

arXiv:2608. 05686v1 Announce Type: cross Abstract: Quantum error correction protects logical information only when every physical operation remains below the fault-tolerance threshold, a condition that must be maintained continuously rather than only at the initial calibration.

By Weiyuan Gong, Hong-Ye Hu
arXiv Machine Learning
Jun 11

Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance

arXiv:2606. 11620v1 Announce Type: cross Abstract: Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process.

By Honjar Xing, Yehong Jiang, Xianbang Wang, Zehua Wang, Zhicheng Jiang