A hybrid classical-quantum approach to speed-up Q-learning.

Area of Science:

• Quantum Computing
• Machine Learning
• Computational Science

Background:

• Reinforcement learning agents often face complex decision-making processes.
• Encoding probability distributions with large support is computationally intensive for classical systems.

Purpose of the Study:

• To introduce a novel classical-quantum hybrid approach for computational enhancement.
• To develop a quantum routine for encoding probability distributions in reinforcement learning.
• To improve the decision-making performance of learning agents.

Main Methods:

• A hybrid classical-quantum computational approach is proposed.
• A quantum routine is developed to encode probability distributions using quantum accelerators.
• The routine is integrated into a reinforcement learning framework, specifically for Q-learning.
• Performance is evaluated based on computational complexity, quantum resource requirements, and accuracy.

Main Results:

• The hybrid approach offers a quadratic performance improvement in agent decision processes.
• The quantum routine efficiently encodes probability distributions with large support.
• The method is suitable for scenarios with a large, finite number of actions.

Conclusions:

• The developed classical-quantum hybrid method provides a significant performance boost for learning agents.
• The quantum routine is a valuable tool for encoding probability distributions in reinforcement learning, particularly in complex scenarios.
• This work demonstrates a practical application of quantum computing in machine learning.