A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
arXiv preprint arXiv:2504.18694 (2025)
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Jaynes-Cummings qubit-boson systems show superior nonlinear memory capacity and comparable Mackey-Glass forecasting performance when used as quantum reservoirs.
citing papers explorer
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Quantum and classical processing with photonic quantum machine learning
A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
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Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction
Jaynes-Cummings qubit-boson systems show superior nonlinear memory capacity and comparable Mackey-Glass forecasting performance when used as quantum reservoirs.