Distributions of noisy expectation values over sets of measurement operators on random mixed states are derived combinatorially and approximated by fitted effective global-depolarizing models that match peaks in brickwork circuit simulations but deviate in tails.
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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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Distributions of Noisy Expectation Values over Sets of Measurement Operators
Distributions of noisy expectation values over sets of measurement operators on random mixed states are derived combinatorially and approximated by fitted effective global-depolarizing models that match peaks in brickwork circuit simulations but deviate in tails.
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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.