arXiv:2501.17041 · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection
Farida Farsian, N. Parmiggiani, Alessandro Rizzo, Gabriele Panebianco, Andrea A. Bulgarelli, Francesco Schillirò, C. Burigana, V. F. Cardone, Luca Cappelli, Massimo Meneghetti, Giuseppe Murante, Giuseppe Sarracino, R. Scaramella, V. Testa, T. Trombetti
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.
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.
Gamma-Ray Bursts (GRBs) are the most luminous explosions in the universe, and are of great interest in astrophysics research. Despite the development of onboard trigger algorithms, significant obstacles remain in identifying weak events and effectively filtering out false detections. These challenges motivate the exploration of innovative technologies such as deep learning and quantum computing to help traditional triggers as a second verification level and increase reliability across the detection pipeline. We present a comparison of a classical and a quantum autoencoder running on simulated quantum hardware, tackling an anomaly detection task in a controlled environment. The goal is to compare their performance, with a focus on resource-constrained scenarios involving limited data and trainable parameters. Both approaches were tested using a simulated set of light curves from the anticoincidence shield of the Compton Spectrometer and Imager (COSI) soft gamma-ray telescope. Short-duration GRB signals were simulated using MEGAlib and then modified through data augmentation techniques. The results indicate that while classical autoencoders reconstruct better, achieving a Mean Squared Error (MSE) as low as 2 . 97 × 1 0 − 5 , the efficiency of the quantum autoencoders is notable with lower numbers of parameters and smaller datasets. Using only 10 trainable parameters and 100 input samples, the quantum model obtained an MSE of 2 . 24 × 1 0 − 4 , outperforming its classical counterpart (MSE of 3 . 81 × 1 0 − 2 with 705 parameters). These results show a promising role for quantum machine learning in astrophysical contexts where data related to GRBs are limited or lightweight model architectures are essential.
Machine Learning (ML) serves as a general-purpose, highly adaptable, and versatile framework for investigating complex systems across domains. However, the resulting computational resource demands, in terms of the number of parameters and the volume of data required to train ML models, can be high, often prohibitive. This is the case in astrophysics, where multimedia space data streams usually have to be analyzed. In this context, quantum computing emerges as a compelling and promising alternative, offering the potential to address these challenges in a feasible way. Specifically, a four-step quantum machine learning (QML) workflow is proposed encompassing data encoding, quantum circuit design, model training and evaluation. Then, focusing on the data encoding step, different techniques and models are investigated within a case study centered on the Gamma-Ray Bursts (GRB) signal detection in the astrophysics domain. The results thus obtained demonstrate the effectiveness of QML in astrophysics, highlighting the critical role of data encoding, which significantly affects the QML model performance.
Quantum computing represents a cutting-edge frontier in artificial intelligence. It makes use of hybrid quantum-classical computation which tries to leverage quantum mechanic principles that allow us to use a different approach to deep learning classification problems. The work presented here falls within the context of the AGILE space mission, launched in 2007 by the Italian Space Agency. We implement different Quantum Convolutional Neural Networks (QCNN) that analyze data acquired by the instruments onboard AGILE to detect Gamma-Ray Bursts from sky maps or light curves. We use several frameworks such as TensorFlow-Quantum, Qiskit and Penny-Lane to simulate a quantum computer. We achieved an accuracy of 95.1% on sky maps with QCNNs, while the classical counterpart achieved 98.8% on the same data, using however hundreds of thousands more parameters.
Convolutional Neural Networks (CNNs) are extensively utilized for image classification due to their ability to exploit data correlations effectively. However, traditional CNNs encounter several challenges, including limitations in accuracy improvement, computational efficiency, explain ability, and generalization. As data dimensionality or model complexity increases, CNNs often become more difficult to train efficiently, leading to slower processing times. To overcome these challenges, Quantum Convolutional Neural Networks (QCNNs) offer a promising quantum-based approach to enhance existing models, particularly by improving training efficiency and reducing processing times. This paper provides a comparative analysis of classical CNNs and a novel quantum circuit architecture tailored for image-based tasks. The analysis emphasizes the adaptability and potential of quantum circuits to enhance feature extraction, recognition accuracy, and processing efficiency.We applied various QCNN architectures to the Chinese-MNIST dataset and compared their performance with traditional CNN models. Although QCNNs achieved slightly lower accuracy, they demonstrated significantly greater stability. These findings high- light QCNNs’ potential to overcome the computational limitations of conventional deep learning, offering a promising pathway for quantum-enhanced solutions in computer vision. Future work will focus on refining quantum circuit designs, developing hybrid quantum-classical models, and extending QCNN applications to larger and more diverse datasets.
Since their discovery in 2007, fast radio bursts (FRBs) have become a major focus of radio astronomy research because they enable researchers to probe the intergalactic medium beyond the Milky Way. Finding more FRBs will enable better understanding of the local and distant universe and the physics that drives these violent ultra-luminous events. Even though various classical machine learning algorithms have been used to search for FRBs, here I show, for the first time, that quantum machine learning algorithms can be used for detecting FRBs in large datasets. Quantum machine learning algorithms are still in their infancy, but show potential to outperform classical machine learning methods. I investigate how effective quantum machine learning, specifically the quantum convolutional neural network (QCNN), can be for identifying FRBs in radio astronomy datasets. The QCNNs developed for this work use 8, 10 and 12 qubits. These models can be run on existing quantum hardware; however, I run classical simulations to test the quantum models in a noise-free environment. Using a new FRB training dataset created for this work, QCNNs were trained, and their performance was evaluated by comparing them to classical machine learning models: the random forest and convolutional neural network. I demonstrate that the best quantum model outperforms the classical models tested. It was observed that performance improved with increasing numbers of qubits. The best QCNN model developed for this work was also applied to real data from the CSIRO Parkes radio telescope Murriyang and was successfully used to find a radio burst from a real astronomical source. This work demonstrates that QCNNs can detect FRBs in real astronomy datasets.
This paper presents a comprehensive evaluation of the potential of Quantum Convolutional Neural Networks (QCNNs) in comparison to classical Convolutional Neural Networks (CNNs) and Artificial / Classical Neural Network (ANN) models. With the increasing amount of data, utilizing computing methods like CNN in real-time has become challenging. QCNNs overcome this challenge by utilizing qubits to represent data in a quantum environment and applying CNN structures to quantum computers. The time and accuracy of QCNNs are compared with classical CNNs and ANN models under different conditions such as batch size and input size. The maximum complexity level that QCNNs can handle in terms of these parameters is also investigated. The analysis shows that QCNNs have the potential to outperform both classical CNNs and ANN models in terms of accuracy and efficiency for certain applications, demonstrating their promise as a powerful tool in the field of machine learning.