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arXiv 1806.04553quant-ph
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带量子边信息的随机性量子概率估计

Quantum Probability Estimation for Randomness with Quantum Side Information

Emanuel Knill, Yanbao Zhang, Honghao Fu

摘要:

我们开发了一个量子版本的概率估计框架[arXiv:1709.06159],用于使用量子边信息生成随机性。我们证明了概率估计的大多数性质对于量子概率估计(QPE)是成立的。这包括恒定误差下的渐近最优性和对数输入熵下的随机扩展。QPE是通过构建依赖于模型的量子估计因子(QEF)来实现的,该因子产生数据条件归一化Rényi幂的统计置信上限。这导致了随机生成的条件最小熵估计。的界限是有效的相关模型的序列的实验试验,而不需要独立的和相同的或固定的行为。量化宽松机制可在实施过程中因应情况的转变而作出调整,而试验亦可随时停止,例如当试验结果令人满意时。可以从熵估计器构造QEF,以从熵累积框架[Dupuis,Fawzi和Renner,arXiv:1607.01796]改进经典量子态的条件最小熵的界限。QEF适用于更大类别的模型,包括允许具有超量子但非信号行为的实验设备的模型和半设备依赖模型。改进的边界与$e^{-κs}$形式的有限数据或误差边界相关,其中$s$是所产生的随机位的数目。我们给出了一个一般的熵估计的最大概率估计,存在于许多配置的基础上建设。对于类的$(k,2,2)$贝尔测试配置,我们提供了直接优化QEF的架构,以克服基于熵估计的建设的局限性。我们获得并应用QEF的例子涉及$(2,2,2)$贝尔测试配置证明有限数据效率的大幅改善。

英文摘要:

We develop a quantum version of the probability estimation framework [arXiv:1709.06159] for randomness generation with quantum side information. We show that most of the properties of probability estimation hold for quantum probability estimation (QPE). This includes asymptotic optimality at constant error and randomness expansion with logarithmic input entropy. QPE is implemented by constructing model-dependent quantum estimation factors (QEFs), which yield statistical confidence upper bounds on data-conditional normalized Rényi powers. This leads to conditional min-entropy estimates for randomness generation. The bounds are valid for relevant models of sequences of experimental trials without requiring independent and identical or stationary behavior. QEFs may be adapted to changing conditions during the sequence and trials can be stopped any time, such as when the results so far are satisfactory. QEFs can be constructed from entropy estimators to improve the bounds for conditional min-entropy of classical-quantum states from the entropy accumulation framework [Dupuis, Fawzi and Renner, arXiv:1607.01796]. QEFs are applicable to a larger class of models, including models permitting experimental devices with super-quantum but non-signaling behaviors and semi-device dependent models. The improved bounds are relevant for finite data or error bounds of the form $e^{-κs}$, where $s$ is the number of random bits produced. We give a general construction of entropy estimators based on maximum probability estimators, which exist for many configurations. For the class of $(k,2,2)$ Bell-test configurations we provide schemas for directly optimizing QEFs to overcome the limitations of entropy-estimator-based constructions. We obtain and apply QEFs for examples involving the $(2,2,2)$ Bell-test configuration to demonstrate substantial improvements in finite-data efficiency.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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