arXiv:2607. 28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale.
By Ran Miao, Rui Luo, Xiaohan Shan, Xiaoming Sun
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
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. 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.
By Tobias Haug, Askery Canabarro, Leandro Aolita
arXiv:2607. 05814v1 Announce Type: cross Abstract: Real-time decoding is a major bottleneck in scaling quantum error correction (QEC) from noisy intermediate-scale quantum (NISQ) devices to fault-tolerant quantum computing.
By Sumit Chongder
The paper investigates how approximate numerical solvers used in recursive state estimation can be repaired using bounded corrections, characterizing when such corrections meet local admissibility tolerances and how they influence finite‑horizon covariance. It derives error identities that separate solve error from gain drift, revealing quartic and sixth‑order contributions to the covariance response. The framework is applied to a power‑grid tolerance study, showing that learned corrections reduce the required conjugate‑gradient iterations, and it demonstrates a unified interface for classical, quantum, and hybrid solvers.
By Yanjun Ji, Dennis Willsch, Orkun \c{S}ensebat, Priyanka Arkalgud Ganeshamurthy, Zhi Pei, M. Sahnawaz Alam, Ivelina Stoyanova, Frank K. Wilhelm, Bo Zhao, Chao Wang, Kristel Michielsen