arXiv Machine Learning

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.

arXiv Machine Learning
Jun 9

Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems

arXiv:2606. 07666v1 Announce Type: cross Abstract: Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates.

By Sumit Chongder (Indian Institute of Technology Jodhpur)
arXiv AI
5d ago

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.

By Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy
Hugging Face Trending Papers
6d ago

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.

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 Machine Learning
Jun 19

Optimal Ansatz-free Hamiltonian Learning In Situ

arXiv:2606. 19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction.

By Taiqi Zhou, Weiyuan Gong