CNOT Minimal Circuit Synthesis: A Reinforcement Learning Approach

TL;DR

This paper presents a reinforcement learning method for minimizing CNOT gates in quantum circuits, outperforming existing algorithms especially for larger circuit sizes.

Contribution

Introduces a novel reinforcement learning approach for CNOT minimization that generalizes across different circuit sizes using a single agent.

Findings

Outperforms state-of-the-art algorithms as circuit size increases

Uses a single RL agent with preprocessing for different sizes

Effective for circuits with up to 15 qubits

Abstract

CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exclusively from CNOT gates. Given their widespread use, it is imperative to minimise the number of CNOT gates employed. This problem, known as CNOT minimisation, remains an open challenge, with its computational complexity yet to be fully characterised. In this work, we introduce a novel reinforcement learning approach to address this task. Instead of training multiple reinforcement learning agents for different circuit sizes, we use a single agent up to a fixed size $m$. Matrices of sizes different from m are preprocessed using either embedding or Gaussian striping. To assess the efficacy of our approach, we trained an agent with m = 8, and evaluated it on matrices of size n that range from 3 to 15. The results we obtained show that our method overperforms the state-of-the-art algorithm as the value of n increases.