CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem

TL;DR

This paper introduces CO-MAP, a reinforcement learning-based method for qubit mapping in quantum compilation, significantly reducing SWAP gate overhead compared to traditional heuristics.

Contribution

It formulates qubit mapping as a combinatorial optimization problem and trains an RL policy to improve quantum circuit compilation efficiency.

Findings

Achieves 65-85% reduction in SWAP overhead on real datasets.

Outperforms conventional techniques in minimizing SWAP gates.

Uses a local search post-processing to further optimize results.

Abstract

A quantum compiler is a critical piece in the quantum computing pipeline since it allows an abstract quantum circuit to be run on a physical quantum computer. One extremely important subproblem in quantum compilation is the generation of a logical to physical qubit mapping. Typically in quantum compilers this step is either implemented as a random or a heuristic based assignment that aims to minimize additional (SWAP) gate overhead in the quantum circuit. In this paper, we present an alternative approach to solving the qubit mapping problem. Specifically, we formulate the qubit mapping problem with a combinatorial optimization (CO) objective. We then present a method to find a solution to the CO problem by training a reinforcement learning (RL) policy. We also propose a local search based post-processing algorithm to further reduce the overhead. Our results show a dramatic improvement over conventional techniques in reducing the number of SWAPs. On different real world datasets like MQTBench and Queko circuits, our trained policy achieves a \textbf{65-85\%} reduction in SWAP overhead when compared to existing quantum compilers.