The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here, we introduce a new technique, based on a machine learning protocol with artificial neural networks, that allows us to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate, and more robust than conventional methods when limited amounts of data are available. Moreover, the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.

中文翻译：

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神经网络检测多模威格纳负电

大希尔伯特空间中量子特征的表征是测试量子协议的关键要求。在连续变量编码中，量子零差层析成像需要的测量量随所涉及模式的数量呈指数增加，这实际上使该协议即使在很少的模式下也难以处理。在这里，我们介绍一种基于带有人工神经网络的机器学习协议的新技术，该技术使我们能够直接检测多模量子态的Wigner函数的负性。我们在一类完整的数值模拟多模量子态上测试了该程序，对于该类态，Wigner函数在解析上是已知的。我们证明了当有限数量的数据可用时，该方法比常规方法更加快速，准确和可靠。此外，