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qcnn

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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.

  • Updated Mar 3, 2026
  • 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.

  • Updated Dec 15, 2025
  • Python

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.

  • Updated May 12, 2025
  • Python

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

  • Updated Mar 14, 2026

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

  • Updated Mar 25, 2026
  • Python

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

  • Updated Mar 25, 2026
  • Python

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