Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm
Abstract
We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .
Cite
@article{arxiv.2210.06411,
title = {Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm},
author = {Tom Rindell and Berat Yenilen and Niklas Halonen and Arttu Pönni and Ilkka Tittonen and Matti Raasakka},
journal= {arXiv preprint arXiv:2210.06411},
year = {2023}
}
Comments
22 pages, 4 figures; version 2 changes: title changed, numerical analysis extended to 1000 random states, references added, other minor improvements, conclusions remain unaltered