Paper ID: 2401.17976

Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning

Arnau Pastor, Pau Escofet, Sahar Ben Rached, Eduard Alarcón, Pere Barlet-Ros, Sergi Abadal

Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architectures remains a significant challenge. Multi-core quantum architectures are proposed to solve the scalability problem, arising a new set of challenges in hardware, communications and compilation, among others. One of these challenges is to adapt a quantum algorithm to fit within the different cores of the quantum computer. This paper presents a novel approach for circuit partitioning using Deep Reinforcement Learning, contributing to the advancement of both quantum computing and graph partitioning. This work is the first step in integrating Deep Reinforcement Learning techniques into Quantum Circuit Mapping, opening the door to a new paradigm of solutions to such problems.

Submitted: Jan 31, 2024