AI methods for approximate compiling of unitaries
This work addresses the challenge of transpiling unitaries for quantum hardware, offering incremental improvements in efficiency for quantum computing applications.
The paper tackles the problem of approximate compiling of unitaries for quantum computing by proposing an AI-driven method that identifies initial templates and predicts parameters, then refines them via gradient descent to maximize fidelity. It demonstrates improvements over exhaustive search and random initialization on 2 and 3-qubit unitaries, showing potential for more efficient quantum computations.
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.