# Coherent Transport of Quantum States by Deep Reinforcement Learning

## TL;DR

This paper demonstrates that deep reinforcement learning can autonomously discover superior control sequences for quantum state transport in quantum dots, outperforming traditional ansatz-based methods and adapting to realistic disturbances.

## Contribution

It introduces a novel application of deep reinforcement learning to quantum control, surpassing traditional ansatz solutions and adapting to disturbances in quantum dot systems.

## Key findings

- RL discovers control sequences outperforming traditional ansatz.
- RL adapts to disturbances, improving speed and fidelity.
- Method enables online updates and better system control.

## Abstract

Some problems in physics can be handled only after a suitable \textit{ansatz }solution has been guessed. Such method is therefore resilient to generalization, resulting of limited scope. The coherent transport by adiabatic passage of a quantum state through an array of semiconductor quantum dots provides a par excellence example of such approach, where it is necessary to introduce its so called counter-intuitive control gate ansatz pulse sequence. Instead, deep reinforcement learning technique has proven to be able to solve very complex sequential decision-making problems involving competition between short-term and long-term rewards, despite a lack of prior knowledge. We show that in the above problem deep reinforcement learning discovers control sequences outperforming the \textit{ansatz} counter-intuitive sequence. Even more interesting, it discovers novel strategies when realistic disturbances affect the ideal system, with better speed and fidelity when energy detuning between the ground states of quantum dots or dephasing are added to the master equation, also mitigating the effects of losses. This method enables online update of realistic systems as the policy convergence is boosted by exploiting the prior knowledge when available. Deep reinforcement learning proves effective to control dynamics of quantum states, and more generally it applies whenever an ansatz solution is unknown or insufficient to effectively treat the problem.