Abstract Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum computers can be used to classify different neutrino event types observed in neutrino telescopes. We investigate two quantum machine learning approaches—neural projected quantum kernels (NPQKs) and quantum convolutional neural networks (QCNNs) — and find that both achieve classification performance comparable to classical machine learning methods across a wide energy range, establishing the feasibility of executing such a task on present-day quantum hardware. By introducing a moment-of-inertia-based encoding scheme that serves as our preprocessing strategy, we enable efficient and scalable learning with large neutrino astronomy datasets. Tested on both simulators and the IBM Strasbourg quantum processor, the NPQK achieves a testing accuracy near 80 % , with robust results above 1 TeV and close agreement between simulation and hardware performance. A simulated QCNN achieves a ∼ 70 % accuracy over the same energy range. These results underscore the promise of quantum machine learning for neutrino astronomy, paving the way for future advances as quantum hardware matures.
Abstract Quantum computers represent a new computational paradigm with steadily improving hardware capabilities. In this article, we present the first study exploring how current quantum computers can be used to classify different neutrino event types observed in neutrino telescopes. We investigate two quantum machine learning approaches—neural projected quantum kernels (NPQKs) and quantum convolutional neural networks (QCNNs) — and find that both achieve classification performance comparable to classical machine learning methods across a wide energy range, establishing the feasibility of executing such a task on present-day quantum hardware. By introducing a moment-of-inertia-based encoding scheme that serves as our preprocessing strategy, we enable efficient and scalable learning with large neutrino astronomy datasets. Tested on both simulators and the IBM Strasbourg quantum processor, the NPQK achieves a testing accuracy near 80 % , with robust results above 1 TeV and close agreement between simulation and hardware performance. A simulated QCNN achieves a ∼ 70 % accuracy over the same energy range. These results underscore the promise of quantum machine learning for neutrino astronomy, paving the way for future advances as quantum hardware matures.
This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed quantum architecture demonstrates an advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to the faster convergence, the QCNN achieves a greater test accuracy compared to CNNs. Based on our results from numerical simulations, it is a promising direction to apply QCNN and other quantum machine learning models to high energy physics and other scientific fields.
In recent years, there has been a growing interest in leveraging the unique properties of quantum computing to develop novel machine learning algorithms and architectures. This research paper presents an investigation of quantum convolutional neural networks (QCNNs), which leverage the unique properties of quantum computing to potentially improve the accuracy and efficiency of image classification tasks. Specifically, the paper explores three different QCNN architectures, including a pure quantum-based QCNN, a hybrid QCNN with a single quantum convolution layer, and a hybrid convolutional architecture with multiple quantum filters. We tested the models on MNIST dataset and the results of the study demonstrate that hybrid architectures that combine quantum and classical processing are more effective than pure quantum-based architectures in image classification tasks. In particular, the third model, the Hybrid Convolution with Multiple Quantum Filters, achieved the highest test set accuracy of 92.7%. The use of multiple quantum filters in conjunction with a classical neural network resulted in enhanced accuracy and efficiency in image classification tasks, highlighting the potential of hybrid architectures for future applications in machine learning tasks.
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.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
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 explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs’ application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs’ utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs’ robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques.