# Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer

Source: [Quantum Deep Learning: Sampling Neural Nets with a Quantum Annealer](<https://www.sota2.com/research/paper/68fa3f6e1e209131ce8ed5e7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:54:26.225Z

Authors: Catherine F. Higham, Adrian Bedford

Published: 2021-07-19T09:35:02.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2107.08710>)

[PDF](<https://arxiv.org/pdf/2107.08710.pdf>)