Multi-particle interference is an essential ingredient for fundamental quantum mechanics phenomena and for quantum information processing to provide a computational advantage, as recently emphasized by boson sampling experiments. Hence, developing a reliable and efficient technique to witness its presence is pivotal in achieving the practical implementation of quantum technologies. Here, we experimentally identify genuine many-body quantum interference via a recent efficient protocol, which exploits statistical signatures at the output of a multimode quantum device. We successfully apply the test to validate three-photon experiments in an integrated photonic circuit, providing an extensive analysis on the resources required to perform it. Moreover, drawing upon established techniques of machine learning, we show how such tools help to identify the—a priori unknown—optimal features to witness these signatures. Our results provide evidence on the efficacy and feasibility of the method, paving the way for its adoption in large-scale implementations.

正如玻色子采样实验最近所强调的那样，多粒子干扰是基本量子力学现象和量子信息处理提供计算优势的基本要素。因此，开发一种可靠，高效的技术来见证其存在对于实现量子技术的实际实施至关重要。在这里，我们通过最近的有效协议实验性地确定了真正的多体量子干扰，该协议利用了多模量子设备输出处的统计签名。我们成功地将该测试用于验证集成光子电路中的三光子实验，从而对执行该测试所需的资源进行了广泛的分析。此外，利用已建立的机器学习技术，我们将展示这些工具如何帮助识别先验未知的最佳特征，以见证这些签名。我们的结果为该方法的有效性和可行性提供了证据，为在大规模实施中采用该方法铺平了道路。