  Machine learning methods have revolutionized the discovery process of new
molecules and materials. However, the intensive training process of neural
networks for molecules with ever-increasing complexity has resulted in
exponential growth in computation cost, leading to long simulation time and
high energy consumption. Photonic chip technology offers an alternative
platform for implementing neural networks with faster data processing and lower
energy usage compared to digital computers. Photonics technology is naturally
capable of implementing complex-valued neural networks at no additional
hardware cost. Here, we demonstrate the capability of photonic neural networks
for predicting the quantum mechanical properties of molecules. To the best of
our knowledge, this work is the first to harness photonic technology for
machine learning applications in computational chemistry and molecular
sciences, such as drug discovery and materials design. We further show that
multiple properties can be learned simultaneously in a photonic chip via a
multi-task regression learning algorithm, which is also the first of its kind
as well, as most previous works focus on implementing a network in the
classification task.
