arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.04735quant-ph
收藏

评估近期光子量子器件的质量

Assessing the quality of near-term photonic quantum devices

Rawad Mezher, Shane Mansfield

阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

对于近期的量子器件，一个重要的挑战是开发有效的方法来证明噪声水平足够低，以允许进行潜在有用的应用。我们提出了这样一种方法定制的光子量子器件组成的单光子源耦合到耦合到光子探测器的线性光学电路。它使用输入大小为$n$（理想情况下为$n$输入光子）的BosonSampling实验的输出统计数据。我们提出了一系列的基准测试针对两个主要的噪声源，即光子损失和可重复性。我们的方法导致一个单一的数字度量，光子品质因数，定义为输出统计数据通过所有测试的输入光子的最大数量。我们提供了强有力的证据表明，通过所有的测试意味着我们的实验是不是有效的经典模拟，通过显示如何有效地模拟嘈杂的BosonSampling现有的经典算法失败的测试。最后，我们表明，玻色子采样实验与平均光子损失率每模式缩放为$o(1)$和平均保真度之间的任何两个单光子状态的$ (1-o(\frac{1}{n^6}))^2$是足够的，以保持通过我们的测试。不出所料，我们的研究结果强调，缩放的方式，避免有效的经典的可模拟性将在某些时候必然需要纠错和缓解。

英文摘要：

For near-term quantum devices, an important challenge is to develop efficient methods to certify that noise levels are low enough to allow potentially useful applications to be carried out. We present such a method tailored to photonic quantum devices consisting of single photon sources coupled to linear optical circuits coupled to photon detectors. It uses the output statistics of BosonSampling experiments with input size $n$ ($n$ input photons in the ideal case). We propose a series of benchmark tests targetting two main sources of noise, namely photon loss and distinguishability. Our method results in a single-number metric, the Photonic Quality Factor, defined as the largest number of input photons for which the output statistics pass all tests. We provide strong evidence that passing all tests implies that our experiments are not efficiently classically simulable, by showing how several existing classical algorithms for efficiently simulating noisy BosonSampling fail the tests. Finally we show that BosonSampling experiments with average photon loss rate per mode scaling as $o(1)$ and average fidelity of $ (1-o(\frac{1}{n^6}))^2$ between any two single photon states is sufficient to keep passing our tests. Unsurprisingly, our results highlight that scaling in a manner that avoids efficient classical simulability will at some point necessarily require error correction and mitigation.

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。

↑