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Quantum states from normalizing flows
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We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows. The use of normalizing flows enables efficient uncorrelated sampling of configurations from the probability distribution defined by the wavefunction, mitigating a major cost of using neural states in simulation. We demonstrate the use of this architecture for both ground-state preparation (for self-interacting particles in a harmonic trap) and real-time evolution (for one-dimensional tunneling). Finally, we detail a procedure for obtaining rigorous estimates of the systematic error when using neural states to approximate quantum evolution.
Forward citations
Cited by 2 Pith papers
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Hydrodynamic Backflow for Easing the Fermion Sign in Finite-Temperature Electron Path Integral Simulations
A semi-analytic hydrodynamic backflow reduces the fermion sign problem by multiple orders of magnitude in 2D finite-temperature electron path integral simulations, enabling energy calculations up to 32 electrons.
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Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
A normalising-flow-assisted neural quantum state method estimates ground state energies of Ising chains with up to 50 spins, matching matrix product states for long-range interactions.
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