Journal
SCIENTIFIC REPORTS
Volume 13, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-023-30910-7
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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.
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.
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