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

Advanced LIF neuron models for PyTorch, featuring dynamic spike probability, adaptive thresholds, synaptic plasticity, neuromodulation, and a novel hybrid quantum mode for Qubit-based spike generation (via PennyLane). Includes membrane potential tracking, classic and quantum-inspired spiking, full PyTorch integration, and interactive Streamlit.

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.