An SDP-based framework computes optimal quantum cloning maps via Choi isomorphism, certifies optimality with duality, and extracts Kraus operators for universal, phase-covariant, asymmetric, and entanglement cloning including higher-order cases.
2053-2563
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VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
The paper derives necessary and sufficient conditions for emergent quantum dynamics as a Bayesian inference problem, validates them via semidefinite programming in paradigmatic cases, and defines a new robustness measure against noise.
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Semidefinite Programming for Optimal Quantum Cloning: A Computational Framework
An SDP-based framework computes optimal quantum cloning maps via Choi isomorphism, certifies optimality with duality, and extracts Kraus operators for universal, phase-covariant, asymmetric, and entanglement cloning including higher-order cases.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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Emergent Quantum Dynamics as a Bayesian Inference Problem: A Critical Analysis
The paper derives necessary and sufficient conditions for emergent quantum dynamics as a Bayesian inference problem, validates them via semidefinite programming in paradigmatic cases, and defines a new robustness measure against noise.