标题： 经典和量子随机过程模型中的强优化和弱优化 显示英文标题

标题： Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes

摘要： 在描述给定随机过程的预测隐马尔可夫模型中，{\epsilon }机在强最小性方面表现优异，因为它使每种基于瑞尼的记忆度量都达到最小值。 量子模型可能更小。 然而，与{\epsilon }机在经典设置中的独特角色不同，在由纯态隐量子马尔可夫模型描述的过程类别中，有一些过程不存在任何强最小模型。 因此，量子记忆优化取决于哪种记忆度量最符合给定的问题情境。 显示英文摘要

摘要： Among the predictive hidden Markov models that describe a given stochastic process, the {\epsilon}-machine is strongly minimal in that it minimizes every R\'enyi-based memory measure. Quantum models can be smaller still. In contrast with the {\epsilon}-machine's unique role in the classical setting, however, among the class of processes described by pure-state hidden quantum Markov models, there are those for which there does not exist any strongly minimal model. Quantum memory optimization then depends on which memory measure best matches a given problem circumstance.