Quantum neuronal nets-a new tool in neural network modelling

Wiesław Sieńko

This paper presents some conjectures on modeling the quantum systems using lossless-orthogonal neural nets. Structure of these nets consists of n compatibly connected (entangled) pairs of neurons-qubits.

Siddhartha Bhattacharyya

Debanjan Konar, Vaneet Aggarwal, Aditya Das Sarma +3 more

Recently, quantum machine learning has been ap-plied to stochastic-based modelling, promising that the inherent uncertainty in quantum computing will be a significant advan-tage, driving neuromorphic computing research to new heights. Spiking Neural Networks (SNNs) and their neuromorphic are gaining popularity due to their inherent ability to process spatial and temporal data. However, learning the interconnection weights is daunting due to the inherent stochastic characteristics of neuron signals and the inherent non-differentiable spike events in classical SNN. This paper introduces a supervised Deep Spiking Quantum Neural Network (DSQ-Net) using a hybrid quantum- classical architecture having the merits of amplitude encoding in a dressed quantum layer. A novel attempt has been made to obviate the challenges in training a classical deep SNN, assisted by a Variational Quantum Circuit (VQC) in the proposed hy-brid quantum-classical framework. The DSQ-Net has undergone thorough validation and benchmarking procedures using the PennyLane Quantum Simulator and the limited volume real IBM Quantum hardware. The experiments have been conducted on images from the MNIST, FashionMNIST, KMNIST, and CIFAR-I0 datasets. Classification accuracy has been reported in the high nineties for unseen noisy test images using the proposed DSQ-Net model on the quantum simulator. It outper-forms its classical counterpart (Deep Spiking Neural Networks), shallow Random Quantum Neural Networks (RQNN), Quantum Superposition-inspired Spiking Neural Networks (SQIN), ResNet- 18, and AlexNet. The PyTorch implementation of DSQ-Net is made available on Github11https://anonymous.4open.science/r/DSQ-Net-037E,

Nasrullah Masud, Abdus Sobhan, Md Tanvir Islam Sourav +1 more

Vinicius Hernandes, Eliška Greplová

Understanding how biological neural networks process information is one of the biggest open scientific questions of our time. Advances in machine learning and artificial neural networks have enabled the modeling of neuronal behavior, but classical models often require a large number of parameters and highly task-specific architectures, which can complicate model design and scalability. Quantum computing offers an alternative approach through quantum machine learning, which can achieve efficient training with fewer parameters. In this work, we introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity. Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods. These findings highlight the potential of quantum generative models to provide new tools for modeling and understanding neuronal behavior, offering a promising avenue for future research in neuroscience.

Michail A. Zak, Colin P. Williams