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Related Experiment Video

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Hybrid quantum-classical convolutional neural network for astrophysical object classification.

Ahmad Rauf1, Javeria Amin2, Jameel-Un Nabi1

  • 1University of Wah, Department of Physics, Wah Cantt. 47040, Pakistan.

Physical Review. E
|February 20, 2026
PubMed
Summary
This summary is machine-generated.

AstroNet, a quantum machine learning model, classifies astronomical objects with high accuracy. It uses quantum feature extraction and a convolutional neural network for efficient analysis of telescope data.

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Area of Science:

  • Astronomy and astrophysics
  • Computer science
  • Quantum computing

Background:

  • Classifying celestial objects is crucial for understanding cosmic evolution.
  • Analyzing vast astronomical datasets from telescopes presents significant challenges.
  • Quantum machine learning (QML) offers a powerful approach for efficient and accurate data processing.

Purpose of the Study:

  • To propose a novel model, AstroNet, for classifying astrophysical objects.
  • To leverage quantum feature extraction combined with a convolutional neural network (CNN).
  • To enhance the analysis of large astronomical datasets.

Main Methods:

  • Developed the AstroNet model, integrating quantum feature extraction with a custom seven-layer CNN.
  • Implemented quantum feature extraction by encoding pixel data into quantum states using qubits.
  • Constructed a quantum circuit with entanglement using CNOT gates and parameterized rotations, simulated via pennylane.
  • Trained the AstroNet model using Adam optimizer, Sparse Categorical Cross-entropy, batch size 32, learning rate 0.0001, and 10 epochs.

Main Results:

  • Achieved a classification performance of up to 0.99 on five benchmark astrophysical datasets.
  • Demonstrated superior performance compared to existing methods in astrophysical object classification.
  • Successfully processed complex image data using quantum states for enhanced representation.

Conclusions:

  • The AstroNet model, combining quantum feature extraction and CNNs, shows significant promise for astrophysical object classification.
  • Quantum-enhanced machine learning provides a viable solution for analyzing large-scale astronomical data.
  • This approach paves the way for more efficient and accurate cosmic exploration.