Classical light training of photonic quantum reservoirs enables accurate model-free estimation of single-qubit observables and two-qubit entanglement witnesses on unseen quantum states.
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A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
citing papers explorer
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Efficient classical training of model-free quantum photonic reservoir
Classical light training of photonic quantum reservoirs enables accurate model-free estimation of single-qubit observables and two-qubit entanglement witnesses on unseen quantum states.
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Measurement-enabled online quantum processing with amplitude encoding
A new protocol for online amplitude-encoded quantum reservoir computing is proposed that uses mid-circuit measurement and reset to implement partial-trace dynamics and indirect measurements for observables.
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Optimal quantum reservoir learning in proximity to universality
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.