使用深度生成模型学习量子光学实验中纠缠的可解释表示
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
摘要：
量子物理实验产生了一些有趣的现象，如干涉或纠缠，这是许多未来量子技术的核心特性。量子实验的设置结构与其纠缠特性之间的复杂关系对于量子光学的基础研究至关重要，但很难直观地理解。我们提出了一个量子光学实验的深度生成模型，其中变分自编码器在量子光学实验设置的数据集上进行训练。在一系列的计算实验中，我们研究了我们的量子光学变分自动编码器（QOVAE）的学习表示及其对量子光学世界的内部理解。我们证明了QOVAE学习量子光学实验的可解释表示以及实验结构和纠缠之间的关系。我们表明，QOVAE能够产生新的实验高度纠缠量子态与特定的分布，匹配其训练数据。QOVAE可以学习产生特定的纠缠态，并有效地搜索产生高度纠缠量子态的实验空间。重要的是，我们能够解释QOVAE如何构造其潜在空间，发现我们可以用量子物理学解释的奇怪模式。结果展示了我们如何在复杂的科学领域中使用和理解深度生成模型的内部表示。QOVAE和我们的研究成果可以立即应用于其他物理系统。
英文摘要：
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present a deep generative model of quantum optics experiments where a variational autoencoder is trained on a dataset of quantum optics experimental setups. In a series of computational experiments, we investigate the learned representation of our Quantum Optics Variational Auto Encoder (QOVAE) and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an interpretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. The QOVAE can learn to generate specific entangled states and efficiently search the space of experiments that produce highly entangled quantum states. Importantly, we are able to interpret how the QOVAE structures its latent space, finding curious patterns that we can explain in terms of quantum physics. The results demonstrate how we can use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems.
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