AI-Powered Algorithm-Centric Quantum Processor Topology Design

TL;DR

This paper introduces a reinforcement learning-based method for designing quantum processor topologies tailored to specific circuits, significantly reducing circuit depth and improving performance on noisy quantum hardware.

Contribution

It presents a novel reinforcement learning approach for dynamic qubit topology design, moving beyond fixed topologies to optimize quantum circuit execution.

Findings

Achieved at least 20% reduction in circuit depth in 60% of cases

Maximum of 46% circuit depth reduction observed

Method scales effectively with increasing circuit size

Abstract

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20\% reduction in circuit depth in 60\% of the cases examined, and a maximum enhancement of up to 46\%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.