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原文連結
論文資訊
- 類型：已發表論文
- 日期：2018-02-13
摘要
We introduce a 量子 algorithm for memory-efficient biased sampling of rare events generated by classical memoryful 隨機 processes. Two efficiency metrics are used to compare 量子 and classical resources for rare-event sampling. For a fixed 隨機 process, the first is the classical-to-量子 ratio of required memory. We show for two example processes that there exists an infinite number of rare-event classes for which the memory ratio for sampling is larger than r, for any large real number r. Then, for a sequence of processes each labeled by an integer size N, we compare how the classical and 量子 required memories scale with N. In this setting, since both memories can diverge as N -> infinity , the efficiency metric tracks how fast they diverge. An extreme 量子 memory advantage exists when the classical m
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