Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly—they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximize memory compression advantages and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favorable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past.

中文翻译：

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具有高效长期记忆的量子自适应代理

自适应系统成功的关键是它们能够解释来自环境的信号并做出相应的反应——它们充当与周围环境交互的代理。当能够执行越来越复杂的策略时，这些代理通常会表现得更好。这是有代价的：智能体必须从过去的经验中回忆的信息越多，它需要的记忆就越多。在这里，我们研究了能够进行量子信息处理的代理的能力。我们揭示了量子代理需要采用的最一般形式，以最大限度地发挥记忆压缩优势并提供编码其记忆状态的系统方法。我们展示了这些编码相对于内存最小的经典代理可以表现出非常有利的缩放优势，特别是当必须保留有关越来越远的过去事件的信息时。