论文标题
用量子变量电路进行加固学习
Reinforcement Learning with Quantum Variational Circuits
论文作者
论文摘要
近年来，量子计算技术的开发与深度强化学习技术的进步相似。这项工作探讨了量子计算促进加强学习问题的潜力。量子计算方法为传统算法提供了重要的时间和空间复杂性，因为它能够利用叠加和纠缠的量子现象。具体而言，我们研究了量子变量电路的使用，这是一种量子机学习的形式。我们介绍了用于编码量子变异电路的经典数据的技术，我们进一步探索了DQN和Double DQN的纯和混合量子算法。我们的结果表明，混合动力和纯量子变异电路具有使用较小的参数空间来解决增强学习任务的能力。这些比较是通过两个OpenAi健身环境进行的：Cartpole和二十一点，这项工作的成功表明了量子机器学习与深度强化学习之间的牢固未来关系。
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.
