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:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.
By Manoranjan Gandhudi, Arunkumar V., G. R. Anil, Gangadharan G. R
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
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:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.
By Kung-Ming Lan
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
arXiv:2511. 12482v2 Announce Type: replace-cross Abstract: Quantum error correction is essential for fault-tolerant quantum computing.
By Yue Yin, Tailong Xiao, Xiaoyang Deng, Ming He, Jianping Fan, Guihua Zeng
The paper proposes reverse n‑wise output‑oriented testing for AI/ML and quantum computing systems, a method that builds covering arrays over output equivalence classes, confidence buckets, decision boundaries, fairness partitions, embedding clusters, ranking stability bands, quantum measurement distributions, and error syndrome patterns. It then uses gradient‑free metaheuristic optimization to solve the inverse mapping problem, generating input configurations or quantum circuit parameters that trigger specific behavioral signatures in opaque models. The framework claims to provide explicit coverage guarantees, higher fault detection rates for calibration, boundary, and error syndromes, improved test suite efficiency, and automated partition discovery for MLOps and quantum validation pipelines.
By Lamine Rihani
arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.
By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi
arXiv:2609.13194v1 Announce Type: cross
Abstract: A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. We te...
By Tesfay Zemuy Gebrekidan, Hadush Hailu Gebrerufael
The paper investigates how an attacker could manipulate an AI adviser to issue harmful quantum error‑correction updates. It identifies an ambiguity in passive syndrome records that can mislead recovery selection and demonstrates that additional calibration measurements can provide the missing sign information needed for safe updates. By introducing a separate evaluator that only accepts updates when calibration uncertainty and drift bounds certify improvement, the authors show through simulations and surface‑code experiments that harmful proposals are rejected while beneficial ones are retained.
By A. Bar{\i}\c{s} \"Ozg\"uler
arXiv:2607. 22516v1 Announce Type: cross Abstract: A central design principle in modern machine learning and artificial intelligence is to align a model's inductive bias with the structure of its input data.
By Peiyong Wang, Udaya Parampalli, Casey R. Myers