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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3verdicts
UNVERDICTED 3representative citing papers
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.
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.
citing papers explorer
-
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.
-
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.
-
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.