Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning

TL;DR

This paper introduces a deep reinforcement learning-based quantum compiler that improves efficiency, scalability, and universality for multi-qubit systems, outperforming existing methods in sequence length and inference time.

Contribution

It presents a novel RL-assisted quantum compiler capable of handling multi-qubit operators with enhanced performance and universality, including inverse-free basis sets.

Findings

Outperforms existing RL-based compilers in sequence length and inference time.

Achieves near-optimal solutions guaranteed by the Solovay-Kitaev theorem.

Demonstrates for the first time RL-based compilation of two-qubit operators.

Abstract

Efficient quantum compiling tactics greatly enhance the capability of quantum computers to execute complicated quantum algorithms. Due to its fundamental importance, a plethora of quantum compilers has been designed in past years. However, there are several caveats to current protocols, which are low optimality, high inference time, limited scalability, and lack of universality. To compensate for these defects, here we devise an efficient and practical quantum compiler assisted by advanced deep reinforcement learning (RL) techniques, i.e., data generation, deep Q-learning, and AQ* search. In this way, our protocol is compatible with various quantum machines and can be used to compile multi-qubit operators. We systematically evaluate the performance of our proposal in compiling quantum operators with both inverse-closed and inverse-free universal basis sets. In the task of single-qubit operator compiling, our proposal outperforms other RL-based quantum compilers in the measure of compiling sequence length and inference time. Meanwhile, the output solution is near-optimal, guaranteed by the Solovay-Kitaev theorem. Notably, for the inverse-free universal basis set, the achieved sequence length complexity is comparable with the inverse-based setting and dramatically advances previous methods. These empirical results contribute to improving the inverse-free Solovay-Kitaev theorem. In addition, for the first time, we demonstrate how to leverage RL-based quantum compilers to accomplish two-qubit operator compiling. The achieved results open an avenue for integrating RL with quantum compiling to unify efficiency and practicality and thus facilitate the exploration of quantum advantages.