Zhong Han-Sen, Peng Li-Chao, Li Yuan, Hu Yi, Li Wei, Qin Jian, Wu Dian, Zhang Weijun, Li Hao, Zhang Lu, Wang Zhen, You Lixing, Jiang Xiao, Li Li, Liu Nai-Le, Dowling Jonathan P, Lu Chao-Yang, Pan Jian-Wei
Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China; CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China.
State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai 200050, China.
Sci Bull (Beijing). 2019 Apr 30;64(8):511-515. doi: 10.1016/j.scib.2019.04.007. Epub 2019 Apr 2.
Gaussian Boson sampling (GBS) provides a highly efficient approach to make use of squeezed states from parametric down-conversion to solve a classically hard-to-solve sampling problem. The GBS protocol not only significantly enhances the photon generation probability, compared to standard Boson sampling with single photon Fock states, but also links to potential applications such as dense subgraph problems and molecular vibronic spectra. Here, we report the first experimental demonstration of GBS using squeezed-state sources with simultaneously high photon indistinguishability and collection efficiency. We implement and validate 3-, 4- and 5-photon GBS with high sampling rates of 832, 163 and 23 kHz, respectively, which is more than 4.4, 12.0, and 29.5 times faster than the previous experiments. Further, we observe a quantum speed-up on a NP-hard optimization problem when comparing with simulated thermal sampler and uniform sampler.
高斯玻色子采样（GBS）提供了一种高效的方法，利用参量下转换产生的压缩态来解决经典情况下难以解决的采样问题。与使用单光子福克态的标准玻色子采样相比，GBS协议不仅显著提高了光子产生概率，还与诸如密集子图问题和分子振动光谱等潜在应用相关联。在此，我们报告了首次使用具有同时高光子不可区分性和收集效率的压缩态源进行GBS的实验演示。我们分别以832、163和23 kHz的高采样率实现并验证了3光子、4光子和5光子GBS，这分别比之前的实验快4.4倍、12.0倍和29.5倍以上。此外，与模拟热采样器和均匀采样器相比，我们在一个NP难优化问题上观察到了量子加速。
