Learning quantum dynamics with latent neural ODEs
release_kz32f5aqenhqhedekrgjuilscy
by
Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw, Alán Aspuru-Guzik
2021
Abstract
The core objective of machine-assisted scientific discovery is to learn
physical laws from experimental data without prior knowledge of the systems in
question. In the area of quantum physics, making progress towards these goals
is significantly more challenging due to the curse of dimensionality as well as
the counter-intuitive nature of quantum mechanics. Here, we present the QNODE,
a latent neural ODE trained on dynamics from closed and open quantum systems.
The QNODE can learn to generate quantum dynamics and extrapolate outside of its
training region that satisfy the von Neumann and time-local Lindblad master
equations for closed and open quantum systems. Furthermore the QNODE
rediscovers quantum mechanical laws such as Heisenberg's uncertainty principle
in a totally data-driven way, without constraints or guidance. Additionally, we
show that trajectories that are generated from the QNODE and are close in its
latent space have similar quantum dynamics while preserving the physics of the
training system.
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