Quantum Methods for Neural Networks and Application to Medical Image Classification

Jonas Landman; Natansh Mathur; Yun Yvonna Li; Martin Strahm; Skander Kazdaghli; Anupam Prakash; Iordanis Kerenidis

doi:10.22331/q-2022-12-22-881

Quantum Methods for Neural Networks and Application to Medical Image Classification

Jonas Landman^1,2, Natansh Mathur^1,3, Yun Yvonna Li⁴, Martin Strahm⁴, Skander Kazdaghli¹, Anupam Prakash¹, and Iordanis Kerenidis^1,2

¹QC Ware, Palo Alto, USA and Paris, France
²IRIF, CNRS - University of Paris, France
³Indian Institute of Technology Roorkee, India
⁴F. Hoffmann La Roche AG

Published:	2022-12-22, volume 6, page 881
Eprint:	arXiv:2212.07389v1
Doi:	https://doi.org/10.22331/q-2022-12-22-881
Citation:	Quantum 6, 881 (2022).

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Abstract

Quantum machine learning techniques have been proposed as a way to potentially enhance performance in machine learning applications.
In this paper, we introduce two new quantum methods for neural networks. The first one is a quantum orthogonal neural network, which is based on a quantum pyramidal circuit as the building block for implementing orthogonal matrix multiplication. We provide an efficient way for training such orthogonal neural networks; novel algorithms are detailed for both classical and quantum hardware, where both are proven to scale asymptotically better than previously known training algorithms.
The second method is quantum-assisted neural networks, where a quantum computer is used to perform inner product estimation for inference and training of classical neural networks.
We then present extensive experiments applied to medical image classification tasks using current state of the art quantum hardware, where we compare different quantum methods with classical ones, on both real quantum hardware and simulators. Our results show that quantum and classical neural networks generates similar level of accuracy, supporting the promise that quantum methods can be useful in solving visual tasks, given the advent of better quantum hardware.

► BibTeX data

@article{Landman2022quantummethods,
  doi = {10.22331/q-2022-12-22-881},
  url = {https://doi.org/10.22331/q-2022-12-22-881},
  title = {Quantum {M}ethods for {N}eural {N}etworks and {A}pplication to {M}edical {I}mage {C}lassification},
  author = {Landman, Jonas and Mathur, Natansh and Li, Yun Yvonna and Strahm, Martin and Kazdaghli, Skander and Prakash, Anupam and Kerenidis, Iordanis},
  journal = {{Quantum}},
  issn = {2521-327X},
  publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
  volume = {6},
  pages = {881},
  month = dec,
  year = {2022}
}

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