piQture: A quantum machine learning library for image processing.

Classifying, auto-encoding and reverse-engineering QUBO matrices

A quantum reinforcement learning framework based on PyTorch and PennyLane.

This repository contains the code to reproduce the results in the paper A Variational algorithm for Quantum Neural Networks, accepted in the International Conference on Computational Science 2020, Quantum Computing track.

Qauntum convolutional neural network in protein distance prediction.

Entanglement characterization of variational quantum circuits using a Matrix Product State simulator and qiskit.

Framework and paper for quantum machine learning ansatzë

🚀 QuantumAI merges Quantum Computing with Artificial Intelligence to revolutionize machine learning, cryptography, and optimization. Leveraging quantum superposition, entanglement, and hybrid AI models, this project pushes the boundaries of computational intelligence. ⚡ Next-gen AI meets quantum power! 💡

A library for the rapid prototyping of hybrid quantum-classical neural networks in speech applications.

Hybrid Quantum-Classical Neural Network + Pure QCNN for 9-class skin cancer classification using MERA-based preprocessing (PCA, HOG, PATCH) and QCNNs implemented with PennyLane and PyTorch, inspired by the paper "Quantum convolutional neural network for image classification" (Chen et al., 2023). Dataset: ISIC 2018.

Controlled interpolation between classical and quantum learning. Binarized Quantum Neural Network benchmark harness for systematic sweeping a quantumness parameter to map learning phase transitions.

Adaptive quantum networks in practice: superposed graph topologies and operator-space spatialization, with reproducible hardware-relevant demos and figures.

Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.

Hybrid Quantum–Classical model for brain tumor classification using Quantum FiLM modulation and ResNet-18. Supports multi-class MRI tumor detection with quantum circuit integration.

🧠 Classify brain tumors using a hybrid QCNN with ResNet for accurate MRI image analysis across multiple categories, including no tumor detection.

PyTorch implementation of "Effective dimension of machine learning models" paper

🧠 Detect brain tumors using a hybrid Quantum + Classical model with MRI images, enhancing accuracy and efficiency in diagnosis through advanced AI.

Master’s thesis research framework for noise-robust hybrid quantum–classical neural networks (HQNNs), evaluating reliability, architecture design, and deployment strategies on NISQ hardware.