This repo contains CUDA-Q Academic materials, including self-paced Jupyter notebook modules for building and optimizing hybrid quantum-classical algorithms using CUDA-Q.

Automated and reproducible benchmarking framework for quantum computing workflows.

A programming language for hybrid AI and quantum computing. Compile-time tensor shape checking, linear quantum types enforcing the No-Cloning Theorem, and native autodiff across classical-quantum boundaries via the Parameter Shift Rule. Transpiles to Python.

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.

Python toolkit for Quantum Singular Value Transformation (QSVT), including polynomial constructions, matrix function workflows, and reproducible tools for research in quantum algorithms and numerical linear algebra.

Hybrid Quantum-Classical Genomics Knowledge Graph Model using Google Cirq. Integrates Variational Quantum Circuits (VQC) and the Dynamic Mixture of Recursions (MoR) paradigm with classical Deep Learning to analyze complex genomic structures and expression data.

Modular Python framework for quantum machine learning using PennyLane, including variational classifiers, quantum kernels, and reproducible workflows for hybrid quantum–classical experiments.

Python toolkit for Variational Quantum Eigensolver (VQE), QPE, and QITE workflows for quantum chemistry simulations using PennyLane, supporting reproducible hybrid quantum–classical experiments, using PennyLane.

Hybrid Quantum–Classical Neural Network (QCNN) for automated brain tumour detection using MRI images. Combines EfficientNet-B0 feature extraction with a 4-qubit PennyLane quantum layer and includes a Gradio-based prediction interface.

Proof-of-concept hybrid quantum-classical neural network classifier on make_moons using Qiskit EstimatorQNN + PyTorch TorchConnector. Achieves 100% test accuracy

Python framework for portfolio optimisation using Variational Quantum Eigensolver (VQE), supporting QUBO formulations, constrained optimisation, and reproducible workflows for hybrid quantum–classical finance experiments.

Amazon Braket is a fully managed quantum computing service that helps researchers and developers explore and build quantum algorithms, test them on quantum circuit simulators, and run them on different quantum hardware technologies. Braket provides access to multiple quantum processors from IonQ, Rigetti, QuEra, Oxford Quantum Circuits, and IQM,...

Companion notebook for A Technical Introduction to Quantum Neural Networks. Four small PennyLane experiments on encoding, depth and trainability, classical baselines, and finite-shot cost.

Hybrid Quantum-Classical SVM with PSO optimization for breast cancer diagnosis. Achieves 95.61% accuracy on Wisconsin dataset.

Vahana Rakshaka — Hybrid Quantum Machine Learning Intrusion Detection System (QML-IDS) for CAN Bus Security in Autonomous Vehicles. Built with Qiskit, PyTorch, and Streamlit.

Quantum-enhanced SAM for skin lesion segmentation on ISIC 2018. Hybrid SAM + Quantum Channel Attention achieves 91.23% IoU.

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

Benchmark hybrid quantum-classical ML (VQC, QSVM) vs classical baselines on financial fraud detection

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