QKSA：量子知识搜索代理--使用量子过程层析成像的资源优化强化学习
QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography
摘要：
在这项研究中，我们扩展了通用的强化学习（URL）代理模型的人工智能的量子环境。从量子信息理论出发，将经典的探索性随机知识搜索Agent KL-KSA的效用函数推广到密度矩阵上的距离测度。量子过程层析成像（QPT）算法形成了环境动力学建模程序的易处理子集。最佳QPT策略是基于可变的成本函数，基于算法的复杂性以及计算资源的复杂性来选择。而不是图灵机，我们估计的成本指标的高级语言，以允许现实的实验。整个代理设计封装在一个自我复制的奎因变异的成本函数的基础上的预测值的最佳策略选择方案。因此，多个代理与帕累托最优QPT政策的发展使用遗传规划，模仿物理理论的发展，每个不同的资源权衡。这个正式的框架被称为量子知识搜索代理（QKSA）。尽管量子强化学习很重要，但与量子机器学习的当前趋势相比，几乎没有量子强化学习模型存在。QKSA是第一个类似于经典URL模型的框架。类似于AIXI-tl是Solomonoff普适归纳的资源受限的主动版本，QKSA是最近提出的基于算法信息的量子力学重建的资源受限的参与式观察者框架。QKSA可用于量子信息理论的模拟和研究。具体来说，我们证明了它可以用来加速量子变分算法，其中包括断层重建作为其积分子程序。
英文摘要：
In this research, we extend the universal reinforcement learning (URL) agent models of artificial general intelligence to quantum environments. The utility function of a classical exploratory stochastic Knowledge Seeking Agent, KL-KSA, is generalized to distance measures from quantum information theory on density matrices. Quantum process tomography (QPT) algorithms form the tractable subset of programs for modeling environmental dynamics. The optimal QPT policy is selected based on a mutable cost function based on algorithmic complexity as well as computational resource complexity. Instead of Turing machines, we estimate the cost metrics on a high-level language to allow realistic experimentation. The entire agent design is encapsulated in a self-replicating quine which mutates the cost function based on the predictive value of the optimal policy choosing scheme. Thus, multiple agents with pareto-optimal QPT policies evolve using genetic programming, mimicking the development of physical theories each with different resource trade-offs. This formal framework is termed Quantum Knowledge Seeking Agent (QKSA). Despite its importance, few quantum reinforcement learning models exist in contrast to the current thrust in quantum machine learning. QKSA is the first proposal for a framework that resembles the classical URL models. Similar to how AIXI-tl is a resource-bounded active version of Solomonoff universal induction, QKSA is a resource-bounded participatory observer framework to the recently proposed algorithmic information-based reconstruction of quantum mechanics. QKSA can be applied for simulating and studying aspects of quantum information theory. Specifically, we demonstrate that it can be used to accelerate quantum variational algorithms which include tomographic reconstruction as its integral subroutine.
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