AI methods for approximate compiling of unitaries

TL;DR

This paper introduces AI-driven methods for approximate quantum circuit compiling, improving efficiency and fidelity in translating unitaries into hardware-compatible gates, demonstrated on small qubit systems.

Contribution

It presents novel AI models for initial template prediction and parameter estimation, enhancing the quantum compiling process over traditional methods.

Findings

AI methods outperform exhaustive search in fidelity

Deep learning models effectively suggest initial templates

Gradient refinement improves circuit accuracy

Abstract

This paper explores artificial intelligence (AI) methods for the approximate compiling of unitaries, focusing on the use of fixed two-qubit gates and arbitrary single-qubit rotations typical in superconducting hardware. Our approach involves three main stages: identifying an initial template that approximates the target unitary, predicting initial parameters for this template, and refining these parameters to maximize the fidelity of the circuit. We propose AI-driven approaches for the first two stages, with a deep learning model that suggests initial templates and an autoencoder-like model that suggests parameter values, which are refined through gradient descent to achieve the desired fidelity. We demonstrate the method on 2 and 3-qubit unitaries, showcasing promising improvements over exhaustive search and random parameter initialization. The results highlight the potential of AI to enhance the transpiling process, supporting more efficient quantum computations on current and future quantum hardware.