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Quantum Simulation

Quantum states from normalizing flows

arXiv
Authors: Scott Lawrence, Arlee Shelby, Yukari Yamauchi

Year

2024

Paper ID

66924

Status

Preprint

Abstract Read

~2 min

Abstract Words

90

Citations

N/A

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Quantum Simulation research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • We introduce an architecture for neural quantum states for many-body quantum-mechanical systems, based on normalizing flows.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2406.02451 [2] arXiv https://arxiv.org/abs/2406.02451 [3] Publisher https://arxiv.org/abs/2406.02451

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