Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer
This work addresses the computational bottleneck of sampling in neural networks for high-resolution image classification, offering a potential speedup for AI applications.
The researchers tackled the problem of slow sampling in deep neural networks by framing them as energy-based models processable on a quantum annealer, achieving a 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 binary nature of the model states. With this novel method we successfully transfer a convolutional neural network to the QPU and show the potential for classification speedup of at least one order of magnitude.