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

被引：0

作者：

Pastor, Arnau ^{[1
]}

Escofet, Pau ^{[1
]}

Ben Rached, Sahar ^{[1
]}

Alarcon, Eduard ^{[1
]}

Barlet-Ros, Pere ^{[1
]}

Abadal, Sergi ^{[1
]}

机构：

[1] Univ Politecn Cataluna, Barcelona, Spain

来源：

2024 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS 2024 | 2024年

基金：

欧洲研究理事会;

关键词：

Quantum Computing; Quantum Circuit Mapping; Multi-Core Quantum Computers; Deep Reinforcement Learning;

D O I：

10.1109/ISCAS58744.2024.10557956

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页数：5

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