LP2B encoding converts Lund plane jet representations into Bloch sphere qubit states, enabling a QTTN that matches classical LundNet performance on polarization tagging and W/top tagging with three orders of magnitude fewer parameters and improved low-data regime results.
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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.
citing papers explorer
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Lund Plane to Bloch (LP2B) Encoding for Object and Polarization Tagging with Quantum Jet Substructure
LP2B encoding converts Lund plane jet representations into Bloch sphere qubit states, enabling a QTTN that matches classical LundNet performance on polarization tagging and W/top tagging with three orders of magnitude fewer parameters and improved low-data regime results.
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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.