@inproceedings{1d2eb536c82749d2807f2e752132ed4a,
title = "Reinforcement Learning for Gate Synthesis in Noisy Quantum Systems",
abstract = "We present a gate synthesis application of the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous control in a noisy quantum environment. Gate synthesis plays a crucial role in quantum computing, and optimizing control strategies for gate synthesis is of significant interest. The proposed approach leverages a DNN-based actor-critic architecture to optimize gate synthesis performance. The effectiveness of the algorithm is evaluated through simulations in a noiseless and noisy quantum environment, demonstrating its ability to achieve high-fidelity gate synthesis, O(10-3), despite the presence of Markovian damping terms. The results highlight the potential of DDPG and reinforcement learning techniques for addressing continuous control challenges in quantum computing applications.",
keywords = "actor-critic model, DDPG, optimal control, quantum gate optimization, reinforcement learning",
author = "Amara Katabarwa and Collin Farquhar and Hyeongrak Choi and Marc Davis and Dirk Englund and Yudong Cao and Mekena Metcalf",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023 ; Conference date: 17-09-2023 Through 22-09-2023",
year = "2023",
doi = "10.1109/QCE57702.2023.10216",
language = "English",
series = "Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "215--218",
editor = "Hausi Muller and Yuri Alexev and Andrea Delgado and Greg Byrd",
booktitle = "Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023",
}