Cyclic inverse design on athermal disordered sphere packings produces an emergent marginally absorbing manifold that encodes return-point memory of the training range through gradient discontinuities.
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A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.
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Learning by training: emergent return-point memory from cyclically tuning disordered sphere packings
Cyclic inverse design on athermal disordered sphere packings produces an emergent marginally absorbing manifold that encodes return-point memory of the training range through gradient discontinuities.
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Quantum Machine Learning for State Tomography Using Classical Data
A variational quantum circuit trained solely on classical measurement outcomes reconstructs diverse quantum states including GHZ, spin-chain ground states, and random circuits with fidelities above 90% on simulators and real NISQ hardware.