论文标题
有效的压缩和贝叶斯表征两光谱频谱
Efficient compressive and Bayesian characterization of biphoton frequency spectra
论文作者
论文摘要
频率键是用于量子信息处理的有前途的工具，但是它们的高维度可以使乏味的表征测量。在这里，我们介绍并比较了压缩感应和贝叶斯平均估计，以恢复纠缠光子对的光谱相关性。使用常规的压缩传感算法，我们重建关节光谱，与等效的栅格扫描相比，测量时间降低了26倍。然后，将自定义贝叶斯模型应用于相同的数据，然后我们还实现了对不确定性的可靠且一致的量化。这些有效的两光子表征方法应提高我们使用频率键编码提供的高度并行性和复杂性的能力。
Frequency-bin qudits constitute a promising tool for quantum information processing, but their high dimensionality can make for tedious characterization measurements. Here we introduce and compare compressive sensing and Bayesian mean estimation for recovering the spectral correlations of entangled photon pairs. Using a conventional compressive sensing algorithm, we reconstruct joint spectra with up to a 26-fold reduction in measurement time compared to the equivalent raster scan. Applying a custom Bayesian model to the same data, we then additionally realize reliable and consistent quantification of uncertainty. These efficient methods of biphoton characterization should advance our ability to use the high degree of parallelism and complexity afforded by frequency-bin encoding.
