Improving Quantum Circuit Synthesis with Machine Learning

• URL: http://arxiv.org/abs/2306.05622v1
• Date: Fri, 9 Jun 2023 01:53:56 GMT
• Title: Improving Quantum Circuit Synthesis with Machine Learning
• Authors: Mathias Weiden, Ed Younis, Justin Kalloor, John Kubiatowicz, and Costin Iancu
• Abstract summary: 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.
• Score: 0.7894596908025954
• License: http://creativecommons.org/licenses/by/4.0/
• 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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