Arbitrary quantum states preparation aided by deep reinforcement learning

TL;DR

This paper demonstrates that deep reinforcement learning can effectively design control trajectories for preparing arbitrary quantum states with high fidelity, robustness, and applicability to multi-qubit systems.

Contribution

It introduces a DRL-based method that integrates initial and target states for efficient quantum state preparation, including multi-objective and multi-initial states.

Findings

Achieves average fidelities of 0.9868 for single-qubit and 0.9556 for two-qubit systems.

Control trajectories are robust against charge and nuclear noise.

Provides a new DRL-based solution for complex quantum control tasks.

Abstract

The preparation of quantum states is essential in the realm of quantum information processing, and the development of efficient methodologies can significantly alleviate the strain on quantum resources. Within the framework of deep reinforcement learning (DRL), we integrate the initial and the target state information within the state preparation task together, so as to realize the control trajectory design between two arbitrary quantum states. Utilizing a semiconductor double quantum dots (DQDs) model, our results demonstrate that the resulting control trajectories can effectively achieve arbitrary quantum state preparation (AQSP) for both single-qubit and two-qubit systems, with average fidelities of 0.9868 and 0.9556 for the test sets, respectively. Furthermore, we consider the noise around the system and the control trajectories exhibit commendable robustness against charge and nuclear noise. Our study not only substantiates the efficacy of DRL in QSP, but also provides a new solution for quantum control tasks of multi-initial and multi-objective states, and is expected to be extended to a wider range of quantum control problems.