We successfully demonstrated the detection of the galaxy with an accuracy of 94% via a quantum machine learning model from a NASA image. We divided the galaxy image from NASA into a small 16x16 image as input data. Then we encoded and train the data with a quantum circuit using the parameterized quantum circuit from the paper Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms, arXiv:1905. 10876. With the circuit with high expressibility, we trained and test our model with Cross-Entropy as our loss function and L-BFGS algorithm for optimization. This algorithm is realized in PyTorch and qiskit machine-learning module. This work shows our capability of classification of galaxy images.
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