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.
翻译:量子物理实验产生干扰或缠绕等有趣的现象,这些现象是未来许多量子技术的核心特性。量子实验的设置结构及其缠绕特性之间的复杂关系对于量子光学基础研究至关重要,但难以直观理解。我们展示了量子光学实验的深重基因模型,在这个模型中,一个变式自动电解码器在量子光学实验设置数据集方面受过培训。在一系列计算实验中,我们调查了我们量子光学变化变异体(QOVAE)及其内部对量子光学世界的理解之间的复杂关系。我们证明,QOVAE实验学会了可解释量子模型的可解释性 量子模型能够产生特定的纠结状态,并有效地搜索实验空间变异性空间世界的域域范围空间,我们能够直接解释 QA值结构的变异性数据,我们能够直接地解释 QA值结构的变异性数据。