A black-box machine learning technique trains continuously-coupled photonic waveguide arrays to implement target unitaries using limited single- and two-photon measurements without requiring detailed internal models.
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New immanant and character-based filters reduce computational cost in bosonic randomized benchmarking while providing simple variance expressions and constant low variance for the character filter.
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Training continuously-coupled reconfigurable photonic chips with quantum machine learning
A black-box machine learning technique trains continuously-coupled photonic waveguide arrays to implement target unitaries using limited single- and two-photon measurements without requiring detailed internal models.
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Kostant relation in filtered randomized benchmarking for passive bosonic devices
New immanant and character-based filters reduce computational cost in bosonic randomized benchmarking while providing simple variance expressions and constant low variance for the character filter.