Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential

TL;DR

This paper demonstrates that deep reinforcement learning can effectively control a nonlinear quantum system with continuous measurement feedback, successfully driving it to a near-ground state and achieving high fidelity in complex quantum states.

Contribution

It introduces a DRL-based feedback control method for nonlinear quantum systems, capable of learning counter-intuitive strategies for state preparation.

Findings

DRL successfully drives the system to the ground state with high fidelity.

The learned strategies are counter-intuitive and effective in complex quantum control.

The approach is applicable to systems with continuous weak measurements.

Abstract

Closed loop quantum control uses measurement to control the dynamics of a quantum system to achieve either a desired target state or target dynamics. In the case when the quantum Hamiltonian is quadratic in ${x}$ and ${p}$, there are known optimal control techniques to drive the dynamics towards particular states e.g. the ground state. However, for nonlinear Hamiltonians such control techniques often fail. We apply Deep Reinforcement Learning (DRL), where an artificial neural agent explores and learns to control the quantum evolution of a highly non-linear system (double well), driving the system towards the ground state with high fidelity. We consider a DRL strategy which is particularly motivated by experiment where the quantum system is continuously but weakly measured. This measurement is then fed back to the neural agent and used for training. We show that the DRL can effectively learn counter-intuitive strategies to cool the system to a nearly-pure `cat' state which has a high overlap fidelity with the true ground state.