论文标题
加尔各答佩斯餐厅中的随机学习问题：古典\＆量子策略
Stochastic Learning in Kolkata Paise Restaurant Problem: Classical \& Quantum Strategies
论文作者
论文摘要
我们将回顾一下随机学习策略的结果，包括古典（一次性和迭代）和Quantum（仅一击），以优化在加尔各答Paise餐厅问题的过去十年中，在过去十年中开发了许多竞争代理商之间可用的多选择资源。除了经典和量子策略的一些严格和近似的分析结果外，使用经典的蒙特卡洛模拟的大多数有趣的结果（到目前为止为经典模型获得）的大多数有趣结果。将讨论所有这些，包括计算机科学的申请（工程Internet工程中的工作或资源分配），运输工程（在线车辆租赁问题），操作研究（对授权搜索问题的优化工作，有效的旅行推销员问题解决方案）等。
We will review the results for stochastic learning strategies, both classical (one-shot and iterative) and quantum (one-shot only), for optimizing the available many-choice resources among a large number of competing agents, developed over the last decade in the context of the Kolkata Paise Restaurant Problem. Apart from a few rigorous and approximate analytical results, both for classical and quantum strategies, most of the interesting results on the phase transition behavior (obtained so far for the classical model) using classical Monte Carlo simulations. All these, including the applications to computer science (job or resource allotments in Internet-of-Things), transport engineering (on-line vehicle hire problems), operation research (optimizing efforts for delegated search problem, efficient solution of Travelling Salesman problem), etc will be discussed.
