  Quantum annealing is a promising paradigm for building practical quantum
computers. Compared to other approaches, quantum annealing technology has been
scaled up to a larger number of qubits. On the other hand, deep learning has
been profoundly successful in pushing the boundaries of AI. It is thus natural
to investigate potentially game changing technologies such as quantum annealers
to augment the capabilities of deep learning. In this work, we explore binary
neural networks, which are lightweight yet powerful models typically intended
for resource constrained devices. Departing from current training regimes for
binary networks that smooth/approximate the activation functions to make the
network differentiable, we devise a quadratic unconstrained binary optimization
formulation for the training problem. While the problem is intractable, i.e.,
the cost to estimate the binary weights scales exponentially with network size,
we show how the problem can be optimized directly on a quantum annealer,
thereby opening up to the potential gains of quantum computing. We
experimentally validated our formulation via simulation and testing on an
actual quantum annealer (D-Wave Advantage), the latter to the extent allowable
by the capacity of current technology.
