量子指纹识别的优缺点

Dmitry Gavinsky, J. Kempe, R. D. Wolf
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引用次数: 32

摘要

我们研究了量子指纹在通信复杂性的同时消息传递(SMP)设置下的能力。Yao最近展示了如何利用指数级开销,通过没有共享随机性的量子SMP协议(qpar -协议)来模拟经典的共享随机性SMP协议。我们的第一个结果是将Yao的模拟扩展到最强可能的模型:每个具有无限共享纠缠的多轮量子协议都可以通过qpar协议模拟,开销为指数级。我们利用我们的技术得到了一种有效的qpar协议,这种协议是通过更有限的模拟无法有效求解的。其次,我们通过与具有最大边界的齐次半空间的排列建立联系来紧密表征量子指纹技术的能力。这些排列已经在计算学习理论中得到了很好的研究,我们使用在这一领域获得的一些强有力的结果来展示量子指纹识别的弱点。特别是,这意味着对于几乎所有功能,量子指纹识别协议都比经典的确定性SMP协议差得多
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strengths and weaknesses of quantum fingerprinting
We study the power of quantum fingerprints in the simultaneous message passing (SMP) setting of communication complexity. Yao recently showed how to simulate, with exponential overhead, classical shared-randomness SMP protocols by means of quantum SMP protocols without shared randomness (Qpar-protocols). Our first result is to extend Yao's simulation to the strongest possible model: every many-round quantum protocol with unlimited shared entanglement can be simulated, with exponential overhead, by Qpar-protocols. We apply our technique to obtain an efficient Qpar-protocol for a function which cannot be efficiently solved through more restricted simulations. Second, we tightly characterize the power of the quantum fingerprinting technique by making a connection to arrangements of homogeneous halfspaces with maximal margin. These arrangements have been well studied in computational learning theory, and we use some strong results obtained in this area to exhibit weaknesses of quantum fingerprinting. In particular, this implies that for almost all functions, quantum fingerprinting protocols are exponentially worse than classical deterministic SMP protocols
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