NTU Quantum Research Group - Quantum CNN Demonstration with the Uses of MIT TorchQuantum
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Updated
Sep 28, 2022 - Python
NTU Quantum Research Group - Quantum CNN Demonstration with the Uses of MIT TorchQuantum
Quantum Convolution Neural Network
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.
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.
Hybrid quantum–classical convolutional neural network (QCNN) for MedMNIST classification, with a modular PyTorch–Qiskit pipeline and reproducible experiments.
QML Benchmarks is a research-driven repository implementing and benchmarking fundamental quantum algorithms and quantum machine learning models including QCNN, QFT, Grover, Shor, HHL, VQE, and QAOA. The project analyzes algorithm scalability, optimization behavior, and robustness under realistic NISQ noise simulations through structured experiments
Quantum Machine Learning (QML) project that predicts suitable crops based on soil and environmental parameters using quantum-enhanced models. Built as a hybrid application combining classical preprocessing with quantum circuits (via Qiskit/PennyLane), this app demonstrates how quantum computing can be applied to real-world agricultural challenges.
🧠 Detect brain tumors using a hybrid Quantum + Classical model with MRI images, enhancing accuracy and efficiency in diagnosis through advanced AI.
Quantum-Hybrid Convolutional Neural Network with data re-uploading
🧠 Classify brain tumors using a hybrid QCNN with ResNet for accurate MRI image analysis across multiple categories, including no tumor detection.
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