Reinforcement Learning with Quantum Variational Circuits
This work addresses reinforcement learning efficiency for AI researchers by proposing a quantum computing approach, but it is incremental as it builds on existing quantum machine learning and reinforcement learning methods.
The paper tackles reinforcement learning problems by exploring quantum variational circuits, showing that both hybrid and pure quantum algorithms can solve tasks like CartPole and Blackjack with a smaller parameter space.
The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.