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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Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
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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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Quantum encodings that preserve persistent homology
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.