We study hitting times of quantum walks on graphs from a machine learning perspective. Given a graph it is difficult to decide if quantum walks give an advantage relative to classical random walks. It was shown that machine learning can help to detect quantum advantage even though quantum walk and random walk dynamics on the graph has never been simulated. Here we describe the procedure by which training data is generated for the machine learning approach, and discuss possible future improvements.
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