论文标题
在存在热耗散的情况下，量子加固学习
Quantum reinforcement learning in the presence of thermal dissipation
论文作者
论文摘要
对热耗散对量子增强学习的影响进行研究。为此，一种非隔离量子加固学习方案适合于热耗散的存在。进行了分析计算以及数值模拟，以获得耗散量不会显着降低量子加固学习方案的性能，以表明足够低的温度，在某些情况下甚至有益。量子加固在热耗散的实验条件下学习为实现能够与不断变化的环境相互作用并适应环境相互作用的量子剂开辟了途径，并与量子技术和机器学习中的许多应用合理化。
A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulations are carried out obtaining evidence that dissipation do not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, being in some cases even beneficial. Quantum reinforcement learning under realistic experimental conditions of thermal dissipation opens an avenue for the realization of quantum agents able to interact with a changing environment, and adapt to it, with plausible many applications inside quantum technologies and machine learning.
