Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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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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Neural network modeling of many-body super- and sub-radiant dynamics
Neural quantum states simulate dissipative many-body emission dynamics for approximately 40 atoms in dense 1D and 2D arrays, revealing prominent subradiant behavior at late times.
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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.
- Distributions of Noisy Expectation Values over Sets of Measurement Operators