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Despite their promise to facilitate new scientific discoveries, theopaqueness of neural networks presents a challenge in interpreting the logicbehind their findings. Here, we use a eXplainable-AI (XAI) technique called$inception$ or $deep$ $dreaming$, which has been invented in machine learningfor computer vision. We use this technique to explore what neural networkslearn about quantum optics experiments. Our story begins by training deepneural networks on the properties of quantum systems. Once trained, we "invert"the neural network -- effectively asking how it imagines a quantum system witha specific property, and how it would continuously modify the quantum system tochange a property. We find that the network can shift the initial distributionof properties of the quantum system, and we can conceptualize the learnedstrategies of the neural network. Interestingly, we find that, in the firstlayers, the neural network identifies simple properties, while in the deeperones, it can identify complex quantum structures and even quantum entanglement.This is in reminiscence of long-understood properties known in computer vision,which we now identify in a complex natural science task. Our approach could beuseful in a more interpretable way to develop new advanced AI-based scientificdiscovery techniques in quantum physics.

Deep Quantum Graph Dreaming: Deciphering Neural Network Insights into Quantum Experiments