量子智能体如何改变哪些策略更复杂
How Quantum Agents Can Change Which Strategies Are More Complex
AI总结：
本文研究了智能体处理量子信息的能力如何从根本上改变策略复杂性的结论，推导了经典与量子智能体得出相反复杂性结论的充分条件，并给出了执行策略所需最小记忆的信息论下界。
摘要：
无论是赢得21点游戏还是在繁忙街道中导航，实现预期结果都需要智能体执行自适应策略，即行动在上下文上依赖于过去事件的策略。在复杂性科学中，这促使将记忆作为复杂性的操作量化指标：给定两种策略，更复杂的策略要求智能体追踪更多关于过去的信息。在此，我们表明，关于复杂性的结论从根本上取决于智能体是否能够处理和存储量子信息。因此，尽管经典智能体可能发现执行策略A比策略B更复杂，但量子智能体却能得出相反的结论。我们推导了产生此类矛盾结论的充分条件，并在多种场景下说明了该现象。作为副产品，我们的结果为任何智能体（经典或量子）执行给定策略所需的最小记忆提供了信息论下界。
英文摘要：
Whether winning blackjack or navigating busy streets, achieving desired outcomes requires agents to execute adaptive strategies, strategies where actions depend contextually on past events. In complexity science, this motivates memory as an operational quantifier of complexity: given two strategies, the more complex one demands the agent to track more about the past. Here, we show that conclusions about complexity fundamentally depend on whether agents can process and store quantum information. Thus, while classical agents might find Strategy A more complex to execute than Strategy B, quantum agents can reach the opposite conclusion. We derive sufficient conditions for such contradictory conclusions and illustrate the phenomenon across multiple scenarios. As a byproduct, our results yield an information-theoretic lower bound on the minimal memory required by any agent - classical or quantum - to execute a given strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
