# Deep Reinforcement Learning for Quantum Gate Control

## TL;DR

This paper introduces a deep reinforcement learning approach using a dueling double deep Q-network to optimize the control parameters for implementing high-precision multi-qubit quantum gates, surpassing traditional methods.

## Contribution

It presents a novel application of deep reinforcement learning to quantum gate control, enabling efficient and gradient-free optimization of control parameters for quantum operations.

## Key findings

- Successfully optimized control parameters for Hadamard and CNOT gates.
- Achieved high precision in quantum gate implementation.
- Outperformed traditional optimal control methods.

## Abstract

How to implement multi-qubit gates efficiently with high precision is essential for realizing universal fault tolerant computing. For a physical system with some external controllable parameters, it is a great challenge to control the time dependence of these parameters to achieve a target multi-qubit gate efficiently and precisely. Here we construct a dueling double deep Q-learning neural network (DDDQN) to find out the optimized time dependence of controllable parameters to implement two typical quantum gates: a single-qubit Hadamard gate and a two-qubit CNOT gate. Compared with traditional optimal control methods, this deep reinforcement learning method can realize efficient and precise gate control without requiring any gradient information during the learning process. This work attempts to pave the way to investigate more quantum control problems with deep reinforcement learning techniques.