  Deep learning models are widely used for various industrial and scientific
applications. Even though these models have achieved considerable success in
recent years, there exists a lack of understanding of the rationale behind
decisions made by such systems in the machine learning community. This problem
of interpretability is further aggravated by the increasing complexity of such
models. This paper utilizes concepts from machine learning, quantum computation
and quantum field theory to demonstrate how a many valued quantum logic system
naturally arises in a specific class of generative deep learning models called
Convolutional Deep Belief Networks. It provides a robust theoretical framework
for constructing deep learning models equipped with the interpretability of
many valued quantum logic systems without compromising their computing
efficiency.
