Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
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In Markovian open quantum systems with bistability, noise-induced stochastic switching limits relaxation and follows an Arrhenius law with inverse system size as effective temperature, distinct from deterministic slow relaxation due to a small Liouvillian gap.
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Getting large-scale quantum neural networks ready for quantum hardware
Physics-informed quantum neural networks trained on noisy measurements can construct nontrivial decision boundaries to classify quantum states via order parameters and are suited for NISQ hardware due to links with Markovian open many-body systems.
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Switching Dynamics of Metastable Open Quantum Systems
In Markovian open quantum systems with bistability, noise-induced stochastic switching limits relaxation and follows an Arrhenius law with inverse system size as effective temperature, distinct from deterministic slow relaxation due to a small Liouvillian gap.