Automatic re-calibration of quantum devices by reinforcement learning

2024-04-16 · T. Crosta, L. Rebón, F. Vilariño, J. M. Matera, M. Bilkis

During their operation, due to shifts in environmental conditions, devices undergo various forms of detuning from their optimal settings. Typically, this is addressed through control loops, which monitor variables and the device performance, to maintain settings at their optimal values. Quantum devices are particularly challenging since their functionality relies on precisely tuning their parameters. At the same time, the detailed modeling of the environmental behavior is often computationally unaffordable, while a direct measure of the parameters defining the system state is costly and introduces extra noise in the mechanism. In this study, we investigate the application of reinforcement learning techniques to develop a model-free control loop for continuous recalibration of quantum device parameters. Furthermore, we explore the advantages of incorporating minimal environmental noise models. As an example, the application to numerical simulations of a Kennedy receiver-based long-distance quantum communication protocol is presented.

📄 PDF Abstract BibTeX arXiv:2404.10726

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning

Quantum Architecture Search via Continual Reinforcement Learning

2021-12-10 · Esther Ye, Samuel Yen-Chi Chen

Quantum computing has promised significant improvement in solving difficult computational tasks over classical computers. Designing quantum circuits for practical use, however, is not a trivial objective and requires exp…

Continual Learning Deep Reinforcement Learning Q-Learning reinforcement-learning+2

Graph Reinforcement Learning for Calibration-Aware Quantum Circuit Routing

2026-06-11 · Yash Vardhan Tomar, Dheeraj Peddireddy arxiv

Quantum circuit routing is a key step in compiling programs for noisy intermediate-scale quantum processors, particularly superconducting devices whose sparse fixed coupling makes routing a central compilation cost. Rout…

Reinforcement Learning

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices

2020-09-30 · V. Nguyen, S. B. Orbell, D. T. Lennon, H. Moon 외

Deep reinforcement learning is an emerging machine learning approach which can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automa…

Decision Making Deep Reinforcement Learning Navigate reinforcement-learning+2

Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning

2023-02-05 · Omar Shindi, Qi Yu, Parth Girdhar, Daoyi Dong

High-fidelity quantum gate design is important for various quantum technologies, such as quantum computation and quantum communication. Numerous control policies for quantum gate design have been proposed given a dynamic…

Deep Reinforcement Learning Model free quantum gate design quantum gate calibration quantum gate design+3

Quantum Adiabatic Algorithm Design using Reinforcement Learning

2018-12-27 · Jian Lin, Zhong Yuan Lai, Xiaopeng Li

Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum adiabatic algorithm design. Our approac…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)