Using Reinforcement Learning to Find the Optimal Quantum Adiabatic Short Cut
Livia Guttieres
(University of Chicago)
Research Project Description.
We demonstrate the design of low entropy state schemes that increase coherent control in quantum evolution through laser cooling. To do so, we construct an example problem of high impulse lasers that mitigates the issue of incoherent jumps due to the structural noise of laser by evolving the system with an optimal evolution trajectory of the system to a desired target state. Our assertion is that such a shortcut optimizes energy conservation by minimizing population dispersion of the particle beam. The performance of the machine is evaluated by taking into consideration the fidelity of two reference lasers. We propose a general machine learning problem that can be used to solve and investigate a variety of quantum state problems.
