Interferometric Neural Networks
Arun Sehrawat
On the one hand, artificial neural networks have many successful applications
in the field of machine learning and optimization. On the other hand,
interferometers are integral parts of any field that deals with waves such as
optics, astronomy, and quantum physics. Here, we introduce neural networks
composed of interferometers and then build generative adversarial networks from
them. Our networks do not have any classical layer and can be realized on
quantum computers or photonic chips. We demonstrate their applicability for
combinatorial optimization, image classification, and image generation. For
combinatorial optimization, our network consistently converges to the global
optimum or remains within a narrow range of it. In multi-class image
classification tasks, our networks achieve accuracies of 93% and 83%. Lastly,
we show their capability to generate images of digits from 0 to 9 as well as
human faces.