Quantum-Enhanced Astronomical Signal Classifier

“Our quantum ML tool classifies cosmic radio signals faster than classical models, helping astronomers detect pulsars and rare anomalies in real time.”

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🔭 About the Project

The Quantum-Enhanced Astronomical Signal Classifier is a hybrid quantum-classical machine learning solution designed to help astronomers efficiently analyze massive radio telescope datasets. 🎯 Goal

To detect and classify cosmic signals—such as pulsars, noise, or unknown anomalies—faster and more accurately than traditional methods. ⚡ How It Works

Classical Preprocessing: Cleans and normalizes frequency, amplitude, and SNR features.

Quantum Feature Encoding: Maps classical features into quantum states using ZZFeatureMap.

Quantum Classifier: Utilizes a Variational Quantum Classifier (VQC) to enhance classification accuracy.

Hybrid Pipeline: Combines classical ML with quantum computation for optimized performance.

Visualization Dashboard: Interactive Streamlit app for uploading data, training, and classifying signals.

🌌 Impact

This project addresses a key challenge in modern astronomy: filtering noise from vast cosmic datasets. By leveraging quantum computing, it reduces analysis time, enabling scientists to focus on rare, high-priority signals that could lead to groundbreaking discoveries.

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