Post-cut metadata from quantum circuit fragments enables high-accuracy inference of algorithm family, cut mechanism, and Hamiltonian structure via machine learning on fragment width, depth, and gate counts.
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Crosstalk patterns between quantum circuits on IBM processors are predictable by circuit type and hardware architecture, with high intra-revision consistency and topological decoupling between lattice types.
A survey of quantum adversarial machine learning covering attacks, countermeasures, theoretical underpinnings, trends, and challenges.
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Post-Cut Metadata Inference Attacks on Quantum Circuit Cutting Pipelines
Post-cut metadata from quantum circuit fragments enables high-accuracy inference of algorithm family, cut mechanism, and Hamiltonian structure via machine learning on fragment width, depth, and gate counts.
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Toward Secure Multitenant Quantum Computing: Circuit Affinity, Crosstalk Patterns, and Grouping Strategies
Crosstalk patterns between quantum circuits on IBM processors are predictable by circuit type and hardware architecture, with high intra-revision consistency and topological decoupling between lattice types.
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Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
A survey of quantum adversarial machine learning covering attacks, countermeasures, theoretical underpinnings, trends, and challenges.