  Binary neural networks, i.e., neural networks whose parameters and
activations are constrained to only two possible values, offer a compelling
avenue for the deployment of deep learning models on energy- and memory-limited
devices. However, their training, architectural design, and hyperparameter
tuning remain challenging as these involve multiple computationally expensive
combinatorial optimization problems. Here we introduce quantum hypernetworks as
a mechanism to train binary neural networks on quantum computers, which unify
the search over parameters, hyperparameters, and architectures in a single
optimization loop. Through classical simulations, we demonstrate that of our
approach effectively finds optimal parameters, hyperparameters and
architectural choices with high probability on classification problems
including a two-dimensional Gaussian dataset and a scaled-down version of the
MNIST handwritten digits. We represent our quantum hypernetworks as variational
quantum circuits, and find that an optimal circuit depth maximizes the
probability of finding performant binary neural networks. Our unified approach
provides an immense scope for other applications in the field of machine
learning.
