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- Title
Control of Qubit Dynamics Using Reinforcement Learning.
- Authors
Koutromanos, Dimitris; Stefanatos, Dionisis; Paspalakis, Emmanuel
- Abstract
The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.
- Subjects
ARTIFICIAL neural networks; QUBITS; DEEP learning; MARKOV processes; QUANTUM computing; QUANTUM states; REINFORCEMENT learning
- Publication
Information (2078-2489), 2024, Vol 15, Issue 5, p272
- ISSN
2078-2489
- Publication type
Article
- DOI
10.3390/info15050272