Revisiting the Mapping of Quantum Circuits: Entering the Multi-Core Era

Abstract

Quantum computing represents a paradigm shift in computation, offering the potential to solve complex problems intractable for classical computers. Although current quantum processors already consist of a few hundred of qubits, their scalability remains a significant challenge. Modular quantum computing architectures have emerged as a promising approach to scale up quantum computing systems. This paper delves into the critical aspects of distributed multi-core quantum computing, focusing on quantum circuit mapping, a fundamental task to successfully execute quantum algorithms across cores while minimizing inter-core communications. We derive the theoretical bounds on the number of non-local communications needed for random quantum circuits and introduce the Hungarian Qubit Assignment (HQA) algorithm, a multi-core mapping algorithm designed to optimize qubit assignments to cores with the aim of reducing inter-core communications. Our exhaustive evaluation of HQA against state-of-the-art circuit mapping algorithms for modular architectures reveals a $4.9\times$ and $1.6\times$ improvement in terms of execution time and non-local communications, respectively, compared to the best performing algorithm. HQA emerges as a very promising scalable approach for mapping quantum circuits into multi-core architectures, positioning it as a valuable tool for harnessing the potential of quantum computing at scale.

arXiv commentAccepted to ACM Transaction in Quantum Computing

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Cited by in corpus (8)

• Distributed Quantum Computing: a Survey
• Route-Forcing: Scalable Quantum Circuit Mapping for Scalable Quantum Computing Architectures
• beSnake: A routing algorithm for scalable spin-qubit architectures
• A Multilevel Framework for Partitioning Quantum Circuits
• TeleSABRE: Layout Synthesis in Multi-Core Quantum Systems with Teleport Interconnect
• On the Impact of Classical and Quantum Communication Networks Upon Modular Quantum Computing Architecture System Performance
• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures
• Compilation Techniques for Spin Qubits in a Shuttling Bus Architecture