  Although classifying topological quantum phases have attracted great
interests, the absence of local order parameter generically makes it
challenging to detect a topological phase transition from experimental data.
Recent advances in machine learning algorithms enable physicists to analyze
experimental data with unprecedented high sensitivities, and identify quantum
phases even in the presence of unavoidable noises. Here, we report a successful
identification of topological phase transitions using a deep convolutional
neural network trained with low signal-to-noise-ratio (SNR) experimental data
obtained in a symmetry-protected topological system of spin-orbit-coupled
fermions. We apply the trained network to unseen data to map out a whole phase
diagram, which predicts the positions of the two topological phase transitions
that are consistent with the results obtained by using the conventional method
on higher SNR data. By visualizing the filters and post-convolutional results
of the convolutional layer, we further find that the CNN uses the same
information to make the classification in the system as the conventional
analysis, namely spin imbalance, but with an advantage concerning SNR. Our work
highlights the potential of machine learning techniques to be used in various
quantum systems.
