Quantum deep learning by sampling neural nets with a quantum annealer

We demonstrate the feasibility of framing a classically learned deep neural network as an energy based model that can be processed on a one-step quantum annealer in order to exploit fast sampling times. We propose approaches to overcome two hurdles for high resolution image classification on a quantum processing unit (QPU): the required number and the binary nature of the model states. With this novel method we successfully transfer a pretrained convolutional neural network to the QPU. By taking advantage of the strengths of quantum annealing, we show the potential for classification speedup of at least one order of magnitude.

Automated summary

We demonstrate the feasibility of using a classically learned deep neural network as an energy based model on a quantum annealer to exploit fast sampling times. We propose solutions for the challenges of high resolution image classification on a quantum processing unit (QPU): the required number of model states and the binary nature of these states. By transferring a pretrained convolutional neural network to the QPU, we show the potential for classification speedup of at least one order of magnitude.