Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer
Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer
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IBM 5Q 量子计算机上 AI 代理决策的实证分析
DOI：
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发表时间：
2018
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通讯作者：
Wei Hu
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文献类型：
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作者：
Wei Hu
最近的一项研究表明，使用离子阱量子处理器可以加快强化学习智能体的决策速度。当外部环境发生变化且智能体需要重新学习时，可以观察到其量子优势。本研究发现该量子硬件系统的一个特点是，它往往会高估用于确定智能体将采取的行动的值。IBM的五量子比特超导量子处理器是一个受欢迎的量子平台。我们的研究有两个目的。首先，我们希望确定IBM的5Q量子计算机在运行该学习智能体时与离子阱处理器相比的硬件特性。其次，通过仔细分析，我们观察到离子阱处理器中用于该智能体的量子电路可以简化。此外，当在IBM的5Q量子处理器上进行测试时，我们简化的电路在先前研究中所考察的一项困难学习任务上表现出比原始电路更优的性能。当需要一个良好的基准来比较性能时，我们也会使用IBM的量子模拟器。随着越来越多的量子硬件设备走出实验室并普遍可供公众使用，我们的工作强调了一个事实，即量子硬件的特性和限制可能会对量子算法的性能产生影响。
A recent work has shown that using an ion trap quantum processor can speed up the decision making of a reinforcement learning agent. Its quantum advantage is observed when the external environment changes, and then agent needs to relearn again. One character of this quantum hardware system discovered in this study is that it tends to overestimate the values used to determine the actions the agent will take. IBM’s five qubit superconducting quantum processor is a popular quantum platform. The aims of our study are twofold. First we want to identify the hardware characteristic features of IBM’s 5Q quantum computer when running this learning agent, compared with the ion trap processor. Second, through careful analysis, we observe that the quantum circuit employed in the ion trap processor for this agent could be simplified. Furthermore, when tested on IBM’s 5Q quantum processor, our simplified circuit demonstrates its enhanced performance over the original circuit on one of the hard learning tasks investigated in the previous work. We also use IBM’s quantum simulator when a good baseline is needed to compare the performances. As more and more quantum hardware devices are moving out of the laboratory and becoming generally available to public use, our work emphasizes the fact that the features and constraints of the quantum hardware could take a toll on the performance of quantum algorithms.