Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation

TL;DR

This paper introduces QFactor-Sample, a quantum machine learning-inspired method that significantly accelerates quantum compilation by replacing costly matrix operations with efficient circuit simulations, achieving up to 69x speedup.

Contribution

The paper proposes QFactor-Sample, a novel quantum compilation technique leveraging QML principles to reduce complexity and improve scalability.

Findings

Achieves an average 69x speedup for circuits with more than 8 qubits.

Demonstrates improved scalability and reduced compile time in quantum compilers.

Discusses impact of optimization on partitioning-based compilation schemes.

Abstract

Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality.