Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection

2025-01-28

AI Summary

This study addresses the classification of gamma-ray burst (GRB) signals in simulated light curves for the Cherenkov Telescope Array Observatory (CTAO), a challenging astrophysical time-series analysis task. Method: We propose the first systematic application of quantum convolutional neural networks (QCNNs) to astrophysical time-series classification. A comparative evaluation framework is introduced, incorporating multiple quantum state encoding schemes—specifically data re-uploading and amplitude encoding—to analyze the trade-off between classification accuracy and circuit complexity with respect to qubit count. The hybrid quantum-classical architecture is implemented using Qiskit and trained on a quantum simulator. Results: Experimental results demonstrate that the QCNN achieves over 90% accuracy in GRB detection—comparable to classical CNNs—while reducing parameter count significantly. Moreover, it exhibits superior robustness and resource efficiency on high-dimensional time-series data, highlighting its potential for real-world CTAO data analysis.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

Problem

• Gamma Ray Bursts
• Signal Classification
• Astronomical Data

Innovation

• Quantum Convolutional Neural Networks
• Astronomy Signal Classification
• Resource-Efficiency