Quantum Methods for Neural Networks and Application to Medical Image Classification

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