Principles of Quantum Reinforcement Learning
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Principles of Quantum Reinforcement Learning.
Rel. Davide Girolami. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
Quantum computation is at the forefront of technological advancements, it exploits the principles of quantum mechanics to perform complex calculations by manipulating quantum bits (qubits). Unlike classical bits, qubits can exist in superposition states, allowing exponentially fewer computational steps than the most efficient classical algorithm for certain problems. Quantum logic operations applied to qubits are called quantum gates. Quantum gates are unitary transformations that operate on qubits in a quantum system. A sequence of quantum gates forms a quantum circuit. Classical reinforcement learning (RL) is a branch of machine learning where an agent learns to make decisions through interaction with an environment.
The agent takes actions, receives feedbacks in the form of rewards or penalties, and aims to learn an optimal strategy, called policy, to maximize cumulative rewards over time
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