Abstract: Image classification was revolutionized by classical neural networks, driven by GPU acceleration. This raises the question of what advantages quantum resources could offer for such tasks.

Interactive Python toolkit for topological quantum neural networks: noise-resilient classification via spin-network encoding, with three real-time visualization GUIs. Controlled interpolation between ...

Quantum-inspired Leaky Integrate-and-Fire (QLIF) neurons for PyTorch, adaptive thresholds, dynamic spike probabilities, synaptic plasticity, neuromodulation, and optional qubit-based spike decisions.

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require. When you purchase through links on our site, we may earn an affiliate ...

Abstract: Hybrid Quantum Neural Networks (HQNNs) offer promising potential of quantum computing while retaining the flexibility of classical deep learning. However, the limitations of Noisy ...

RIT and the University of Rochester will receive $2 million in federal funding to further develop the Rochester Quantum Network (RoQNET). The National Institute of Standards and Technology (NIST) is ...