  Large machine learning models based on Convolutional Neural Networks (CNNs)
with rapidly increasing number of parameters, trained with massive amounts of
data, are being deployed in a wide array of computer vision tasks from
self-driving cars to medical imaging. The insatiable demand for computing
resources required to train these models is fast outpacing the advancement of
classical computing hardware, and new frameworks including Optical Neural
Networks (ONNs) and quantum computing are being explored as future
alternatives.
  In this work, we report a novel quantum computing based deep learning model,
the Quantum Optical Convolutional Neural Network (QOCNN), to alleviate the
computational bottleneck in future computer vision applications. Using the
popular MNIST dataset, we have benchmarked this new architecture against a
traditional CNN based on the seminal LeNet model. We have also compared the
performance with previously reported ONNs, namely the GridNet and ComplexNet,
as well as a Quantum Optical Neural Network (QONN) that we built by combining
the ComplexNet with quantum based sinusoidal nonlinearities. In essence, our
work extends the prior research on QONN by adding quantum convolution and
pooling layers preceding it.
  We have evaluated all the models by determining their accuracies, confusion
matrices, Receiver Operating Characteristic (ROC) curves, and Matthews
Correlation Coefficients. The performance of the models were similar overall,
and the ROC curves indicated that the new QOCNN model is robust. Finally, we
estimated the gains in computational efficiencies from executing this novel
framework on a quantum computer. We conclude that switching to a quantum
computing based approach to deep learning may result in comparable accuracies
to classical models, while achieving unprecedented boosts in computational
performances and drastic reduction in power consumption.
