A Probabilistic Graphical Model of Quantum Systems
A Probabilistic Graphical Model of Quantum Systems
复制标题
量子系统的概率图形模型
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
Chen
中科院分区：
文献类型：
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作者：
Chen
量子系统是未来计算和信息处理设备的有希望的候选者。在一个大系统中，关于量子态和过程的信息可能是不完整和分散的。为了整合分布式信息，我们提出了一个量子版本的概率图形模型。模型中的变量（量子态和测量结果）由几种类型的算子（幺正算子、测量算子和合并/分裂算子）联系起来。我们提出了三个机器学习任务的算法在量子概率图模型：一个信念传播算法的未知状态的推断，一个迭代算法的参数值和隐藏状态的同时估计，和一个主动学习算法来选择测量算子的基础上观察到的证据。我们验证这些算法的模拟数据，并指出未来的扩展到一个更全面的理论量子概率图形模型。
Quantum systems are promising candidates of future computing and information processing devices. In a large system, information about the quantum states and processes may be incomplete and scattered. To integrate the distributed information we propose a quantum version of probabilistic graphical models. Variables in the model (quantum states and measurement outcomes) are linked by several types of operators (unitary, measurement, and merge/split operators). We propose algorithms for three machine learning tasks in quantum probabilistic graphical models: a belief propagation algorithm for inference of unknown states, an iterative algorithm for simultaneous estimation of parameter values and hidden states, and an active learning algorithm to select measurement operators based on observed evidence. We validate these algorithms on simulated data and point out future extensions toward a more comprehensive theory of quantum probabilistic graphical models.