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Kyushu University

Academic institutionasia · jp
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Research library314linked papers
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Selected work

Representative Papers

Q3DE: A fault-tolerant quantum computer architecture for multi-bit burst errors by cosmic rays

Oct 01, 2022Micro

Multi-bit burst errors (MBBEs) induced by cosmic rays severely compromise the scalability of fault-tolerant quantum computing. Method: This paper proposes Q3DE, a low-overhead fault-tolerance enhancement architecture built within the surface code framework. Its core innovation is the first syndrome-based, transparent MBBE detection mechanism, integrated with dynamic logical encoding reconstruction and rollback-aware decoding—enabling real-time anomaly identification, decoding rollback, and recovery operation re-evaluation without hardware redundancy. Contribution/Results: By jointly optimizing dynamic code deformation and decoding, Q3DE reduces MBBE duration by 1000× and shrinks the affected qubit region by 50%, substantially alleviating stringent constraints on physical qubit density and chip footprint. This establishes a new paradigm for designing highly reliable, large-scale quantum processors.

15 citations3 influentialRead paper

Embedded Inter-Subject Variability in Adversarial Learning for Inertial Sensor-Based Human Activity Recognition

Aug 31, 2025International Workshop on Machine Learning for Signal Processing

This work addresses the challenge of poor cross-user generalization in inertial sensor-based human activity recognition (HAR) caused by inter-individual variability. To mitigate this issue, the authors propose a novel deep adversarial learning framework that explicitly models and embeds inter-subject differences within the adversarial training process, thereby learning user-invariant feature representations. By enhancing subject invariance, the method effectively reduces the influence of individual-specific characteristics in the learned features. Experimental evaluation on three widely used HAR datasets under leave-one-subject-out (LOSO) cross-validation demonstrates that the proposed approach significantly diminishes inter-subject discrepancies in the feature space and achieves superior recognition accuracy compared to existing state-of-the-art methods in cross-user scenarios.

1 citationsRead paper

A Coverage-Guided Testing Framework for Quantum Neural Networks

Nov 03, 2024arXiv.org

Verifying the correctness of quantum neural networks (QNNs) faces fundamental challenges due to non-classical quantum phenomena—such as superposition and entanglement—that defy conventional testing paradigms; existing methods lack coverage metrics for quantum state evolution. To address this, we propose QCov, the first quantum-aware test coverage criterion tailored for QNNs, which formally incorporates superposition and entanglement dynamics into test adequacy assessment, systematically quantifying coverage over the quantum state space. Leveraging QCov, we design a coverage-driven fuzzing framework that significantly improves defect detection across multiple QNN benchmarks (average +32.7%) and effectively guides the generation of high-coverage inputs, thereby enhancing model robustness and reliability. QCov establishes the first formal foundation for coverage-based testing of QNNs, bridging a critical gap in quantum machine learning verification and introducing a novel paradigm for trustworthy quantum AI systems.

1 citationsRead paper
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