Improving Quantum Circuit Synthesis with Machine Learning

TL;DR

This paper introduces QSeed, a machine learning-enhanced quantum circuit synthesis method that significantly accelerates the process while maintaining low gate counts, especially for complex unitaries like those in Shor's algorithm.

Contribution

The paper presents QSeed, a novel machine learning-based seeded synthesis algorithm that speeds up quantum circuit synthesis by 3.7 times while preserving circuit efficiency.

Findings

QSeed achieves a 3.7x speedup over existing methods.

QSeed maintains low gate counts in synthesized circuits.

Performance generalizes to unseen circuit families.

Abstract

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that minimize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of $3.7\times$ in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process.