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arXiv 2402.18809quant-ph
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学习玻色子随机位移信道的纠缠赋能优势

Entanglement-enabled advantage for learning a bosonic random displacement channel

Changhun Oh, Senrui Chen, Yat Wong, Sisi Zhou, Hsin-Yuan Huang, Jens A. H. Nielsen, Zheng-Hao Liu, Jonas S. Neergaard-Nielsen, Ulrik L. Andersen, Liang Jiang, John Preskill

AI总结:

针对玻色子随机位移信道学习任务,论文证明无纠缠策略需指数于模式数的样本,而纠缠辅助方案在足够压缩下样本数与模式数无关,并在光子损失下仍保持显著优势。

摘要:

我们证明,量子纠缠能够在学习玻色子连续变量(CV)系统的性质方面提供指数级优势。我们考虑的任务是估计作用于 $n$ 个玻色子模式上的位移算子的概率混合,称为随机位移信道。我们证明,如果这 $n$ 个模式没有与辅助量子存储器纠缠,则必须对该信道进行随 $n$ 指数增长的采样,才能以合理精度估计其特征函数;这一样本复杂度下界即使在信道输入和对信道输出执行的测量可自适应选择时仍然成立。另一方面,我们提出一种简单的纠缠辅助方案,在具有足够压缩量的情况下,只需要与 $n$ 无关的样本数。这建立了样本复杂度上的指数级分离。随后,我们分析光子损失的影响,并证明在温和的实验条件下,该纠缠辅助方案仍显著优于任何无损失、无纠缠的方案。我们的工作阐明了纠缠在学习连续变量系统中的作用,并指出了利用 CV 量子平台进行可证明的纠缠赋能优势的实验可行演示方向。

英文摘要:

We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a probabilistic mixture of displacement operators acting on $n$ bosonic modes, called a random displacement channel. We prove that if the $n$ modes are not entangled with an ancillary quantum memory, then the channel must be sampled a number of times exponential in $n$ in order to estimate its characteristic function to reasonable precision; this lower bound on sample complexity applies even if the channel inputs and measurements performed on channel outputs are chosen adaptively. On the other hand, we present a simple entanglement-assisted scheme that only requires a number of samples independent of $n$, given a sufficient amount of squeezing. This establishes an exponential separation in sample complexity. We then analyze the effect of photon loss and show that the entanglement-assisted scheme is still significantly more efficient than any lossless entanglement-free scheme under mild experimental conditions. Our work illuminates the role of entanglement in learning continuous-variable systems and points toward experimentally feasible demonstrations of provable entanglement-enabled advantage using CV quantum platforms.

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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