Quantum-enhanced policy iteration on the example of a mountain car

Advances in the experimental demonstration of quantum processors have provoked a surge of interest to the idea of practical implementation of quantum computing over last years. It is expected that the use of quantum algorithms will significantly speed up the solution to certain problems in numerical...

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Bibliographic Details
Published inarXiv.org
Main Authors Nuzhin, Egor E, Yudin, Dmitry
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.08.2023
Subjects
Algorithms
Iterative algorithms
Iterative methods
Machine learning
Mountains
Optimization
Quantum computing
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Summary:Advances in the experimental demonstration of quantum processors have provoked a surge of interest to the idea of practical implementation of quantum computing over last years. It is expected that the use of quantum algorithms will significantly speed up the solution to certain problems in numerical optimization and machine learning. In this paper, we propose a quantum-enhanced policy iteration (QEPI) algorithm as widely used in the domain of reinforcement learning and validate it with the focus on the mountain car problem. In practice, we elaborate on the soft version of the value iteration algorithm, which is beneficial for policy interpretation, and discuss the stochastic discretization technique in the context of continuous state reinforcement learning problems for the purposes of QEPI. The complexity of the algorithm is analyzed for dense and (typical) sparse cases. Numerical results on the example of a mountain car with the use of a quantum emulator verify the developed procedures and benchmark the QEPI performance.
ISSN:2331-8422