Quantum Methods for Neural Networks and Application to Medical Image Classification

Jonas Landman1,2, Natansh Mathur1,3, Yun Yvonna Li4, Martin Strahm4, Skander Kazdaghli1, Anupam Prakash1, and Iordanis Kerenidis1,2

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

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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.

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