hybrid-quantum-classical

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 Hybrid Quantum-Classical QM/MM Simulation Framework

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 neural network for passive OS fingerprinting — 20-qubit PennyLane variational circuit with a PyTorch head, trained on nPrint packet features

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

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

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

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

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.

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.

Q-OPS Global: a trusted selective quantum–classical copilot for Human–AI staffing—classical scheduling first, constrained QAOA only on ambiguous feasible cores, and classical certification always. Vanguard/WISER Quantum Challenge 2026.

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 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.

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

HQC-Orch: a middleware framework that dynamically routes hybrid quantum-classical ML workloads between QPUs and classical simulators based on circuit depth, noise sensitivity, and latency deadlines. Achieves up to 57% latency reduction and 100%→30% QPU-utilization scaling.

D-Wave — independent third-party profile of a public API surface, by API Evangelist. D-Wave Quantum Inc. (NYSE: QBTS) is the leader in commercial quantum annealing computing and developer of the Advantage and Advantage2 quantum systems. D-Wave's Leap quantum cloud service provides real-time access to D-Wave QPUs and to the Leap hybrid solver family

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

Hybrid classical–quantum neural network experiments on the AFIR-10 dataset (research-oriented, documents limitations & tooling issues)