A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.

Reimplementing the hybrid quantum-classical models from the paper "Quantum machine learning for image classification" by Arsenii Senokosov et al (https://arxiv.org/pdf/2304.09224.pdf)

This work explores whether the quanvolution neural network is explainable by proposing a novel mathematical approach for quantifying explainability

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

Creating a QNN to predict trends in the stock market

Demo of quantum cryptanalysis combining Shor's Algorithm for RSA factorization with Quantum Neural Networks for cryptographic security assessment, running on real IBM quantum computers.

🐙 Quantum proof-of-concept combining Shor's Algorithm and Quantum Neural Networks to factor RSA and assess cryptographic risk using Qiskit on IBM Quantum devices.