Quantum computers enable exponentially better scaling in the number of modes n for learning n-mode Gaussian optical states, with polynomially improved energy dependence over continuous-variable classical shadows.
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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 Gaussian optical states with quantum computers
Quantum computers enable exponentially better scaling in the number of modes n for learning n-mode Gaussian optical states, with polynomially improved energy dependence over continuous-variable classical shadows.
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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.