Derives symmetric Stinespring dilations and covariance constraints for Pauli channels and semigroups to enable explicit time-dependent constructions for quantum simulation.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Symmetric dilations of Pauli channels and semigroups
Derives symmetric Stinespring dilations and covariance constraints for Pauli channels and semigroups to enable explicit time-dependent constructions for quantum simulation.
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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.