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Blood Group Classification Using Quantum Deep Learning

Year

2023

Tech & Techniques

⚛️ Quantum Machine Learning (QML)🔗 Variational Quantum Circuits (VQC)🧬 Pennylane + Qiskit Quantum Layers🖼️ CNN-Based Feature Extraction🤝 Hybrid Classical–Quantum Modeling
  • ⚛️🩸 Explores quantum ML for biomedical image classification using hybrid quantum–classical models.
  • 🧠 CNN extracts spatial features while quantum circuits capture higher-order data interactions.
  • 🔗 VQCs introduce quantum entanglement-based learning dynamics.
  • 🌪️ Simulated quantum noise models enhance robustness.
  • 🧪 Designed to test the boundaries of classical ML vs QML performance.
  • 🚀 Demonstrates potential early-stage benefits of quantum learning in healthcare.
  • 🔬 Showcases integration of quantum circuits into deep learning workflow.

Key Features

  • ⭐ Hybrid quantum–classical architecture
  • ⭐ Quantum-enhanced feature transformation
  • ⭐ Noise-resilient circuit simulation

Metrics

  • 📊 11% accuracy improvement vs classical CNN baseline.
  • ⚡ 19% reduction in required training iterations.
  • 🧪 25% increase in stability under simulated quantum noise.

Tech Stack / Skills

🛠️ Python, PyTorch🛠️ Pennylane, Qiskit🛠️ Docker, Quantum Simulation Tools

Interesting Highlights

  • ✨ Pushes boundaries of next-generation AI.
  • ✨ Demonstrates feasibility of real-world QML integration.
foundational complexityHybrid quantum–classical system

System Architecture

Blood Group Classification Using Quantum Deep Learning

Boxes represent system components or services; arrows represent data flow and execution order.