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arXiv · 2306.05622

Improving Quantum Circuit Synthesis with Machine Learning

Mathias Weiden·Ed Younis·Justin Kalloor·John Kubiatowicz·Costin Iancu
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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.

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Mathias Weiden, Ed Younis, Justin Kalloor, John Kubiatowicz, Costin Iancu. 2023-06-09. Improving Quantum Circuit Synthesis with Machine Learning. https://arxiv.org/abs/2306.05622

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