Random Quantum Neural Networks (RQNN) for Noisy Image Recog- nition
| Author | Konar D.; Gelenbe E.; Bhandary S.; Sarma A.; Cangi A. |
|---|---|
| Title | Random Quantum Neural Networks (RQNN) for Noisy Image Recog- nition |
| Journal | IEEE Xplore |
| Year | 2022 |
| Status | Published |
| DOI | 10.1109/QCE57702.2023.10240 |
| Abstract | <p>Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision<br /> making, signal processing, and image recognition tasks. However, their implementation has been<br /> limited to deterministic digital systems that output probability distributions in lieu of stochastic be-<br /> haviors of random spiking signals. We introduce the novel class of supervised Random Quantum<br /> Neural Networks (RQNNs) with a robust training strategy to better exploit the random nature of<br /> the spiking RNN. The proposed RQNN employs hybrid classical-quantum algorithms with super-<br /> position state and amplitude encoding features, inspired by quantum information theory and the<br /> brain’s spatial-temporal stochastic spiking property of neuron information encoding. We have ex-<br /> tensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via<br /> the PennyLane Quantum simulator with a limited number of qubits. Experiments on the MNIST,<br /> FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an av-<br /> erage classification accuracy of 94.9%. Additionally, the experimental findings illustrate the proposed<br /> RQNN’s effectiveness and resilience in noisy settings, with enhanced image classification accuracy<br /> when compared to the classical counterparts (RNNs), classical Spiking Neural Networks (SNNs), and<br /> the classical convolutional neural network (AlexNet). Furthermore, the RQNN can deal with noise,<br /> which is useful for various applications, including computer vision in NISQ devices. The PyTorch<br /> code 1 is made available on GitHub to reproduce the results reported in this manuscript.</p> |