Quantum computing for astrophysics and cosmology: Results from the INAF activities
Andrea A. Bulgarelli, C. Burigana, Luca Cappelli, V. F. Cardone, S. Cavuoti, Farida Farsian, Irene Graziotti, Antonio Macaluso, Massimo Meneghetti, Giuseppe Murante, N. Parmiggiani, Giuseppe Riccio, Alessandro Rizzo, R. Scaramella, Francesco Schillirò, Giuseppe Sarracino, V. Testa, T. Trombetti
Quantum computing is emerging as a promising tool for astrophysics and cosmology, with the potential to address computationally intensive problems more efficiently than classical approaches. In this review, we report recent advances from the INAF Spoke-10 initiative within the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), focused on quantum algorithms and machine learning techniques for astronomical data analysis. We concentrate on two application areas: (1) quantum machine learning for high-energy transients, specifically the detection of Gamma-Ray Bursts (GRBs) with quantum deep-learning models; and (2) quantum algorithms for cosmology, including Quantum Markov Chain Monte Carlo and Quantum Genetic Algorithms, together with developments of Quantum Fourier Transform methods. Quantum machine learning models for GRB detection, such as autoencoders, are tested on simulated space telescope data, achieving performance comparable to classical deep learning methods and indicating potential benefits in data-limited or constrained scenarios. For cosmology, hybrid quantum–classical algorithms have been developed to determine best-fit parameters, sample posterior distributions for standard cosmological models, and perform Quantum Fast Fourier transforms for the Cosmic Microwave Background (CMB) radiation. These methods are validated on benchmark problems, providing results consistent with established classical methods. We discuss the implications for astrophysical and cosmological analysis. While the field is still in its early stages and no quantum advantage has yet been demonstrated in the presented problems, the progress summarized here highlights both the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices and the open challenges. For quantum machine learning, we demonstrated that these algorithms are more effective when data is scarce or highly complex, which might hint at future quantum advantages. We outline future directions for integrating quantum processors into astronomical pipelines and the steps required to realize practical quantum advantages in data-intensive astrophysics and cosmology.
Quantum computing is emerging as a promising tool for astrophysics and cosmology, with the potential to address computationally intensive problems more efficiently than classical approaches. In this review, we report recent advances from the INAF Spoke-10 initiative within the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing (ICSC), focused on quantum algorithms and machine learning techniques for astronomical data analysis. We concentrate on two application areas: (1) quantum machine learning for high-energy transients, specifically the detection of Gamma-Ray Bursts (GRBs) with quantum deep-learning models; and (2) quantum algorithms for cosmology, including Quantum Markov Chain Monte Carlo and Quantum Genetic Algorithms, together with developments of Quantum Fourier Transform methods. Quantum machine learning models for GRB detection, such as autoencoders, are tested on simulated space telescope data, achieving performance comparable to classical deep learning methods and indicating potential benefits in data-limited or constrained scenarios. For cosmology, hybrid quantum–classical algorithms have been developed to determine best-fit parameters, sample posterior distributions for standard cosmological models, and perform Quantum Fast Fourier transforms for the Cosmic Microwave Background (CMB) radiation. These methods are validated on benchmark problems, providing results consistent with established classical methods. We discuss the implications for astrophysical and cosmological analysis. While the field is still in its early stages and no quantum advantage has yet been demonstrated in the presented problems, the progress summarized here highlights both the current capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices and the open challenges. For quantum machine learning, we demonstrated that these algorithms are more effective when data is scarce or highly complex, which might hint at future quantum advantages. We outline future directions for integrating quantum processors into astronomical pipelines and the steps required to realize practical quantum advantages in data-intensive astrophysics and cosmology.
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 has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
Quantum computers acting as ML accelerators have attracted considerable interest as a promising avenue for addressing the limitations of classical computing confronted with solving complex, high-dimensional problems and handling large data sets. The most exciting aspect of quantum computing lies in its quantum advantage via techniques such as quantum parallelism, superposition, and entanglement which allows not only for rapid data processing, but also a means to resolve complex optimization problems beyond the capacity of classical algorithms. Nevertheless, the usage of quantum computing in machine learning is still a fledgling domain, with challenges concerning quantum noise, hardware restrictions, and algorithmic development. In particular, this research is concerned with the development of and study on hybrid quantum-classical algorithms that target to improve ML models in terms of computational capabilities by making use of the best of both worlds. To address this, researchers are developing hybrid algorithms that leverage the strengths of quantum computing for specific tasks, like optimization and pattern recognition, while still relying on classical computing for more flexible and larger-scale processing, ultimately achieving more efficient machine learning systems that can handle large datasets and complex problem-solving more effectively. It includes potential hybrid approaches like quantum-enhanced optimization algorithms, quantum neural networks, and quantum support vector machines, and examines how such algorithms can be used for practical ML problems including data classification, regression and clustering. Furthermore, the paper proposes that hybrid quantum algorithms offer several advantages over their classical counterparts, such as faster convergence rates, higher accuracy in high-dimensional spaces, and improved handling of non-linear and complex problem landscapes. Key advancements in the area of data processing speed and efficiency in problem-solving with and without quantum in the machine learning workflow are also highlighted in the research through comparative analysis and simulation studies. Lastly, the paper concludes by outlining the obstacles yet to be surmounted and the opportunities that lie ahead for hybrid quantum-classical algorithms, such as further improvement of quantum hardware and error mitigation methods, as well as the design of new algorithmic paradigms that seamlessly integrates the classical and quantum computing spaces.
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.
Some of the most significant achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that continually push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated to understand how the community can benefit from the advantages of quantum information science. Nonetheless, to unleash the full potential of quantum computing, there is a need to understand the quantum behavior and, thus, scale up current algorithms beyond what can be simulated in classical processors. In this work, we explore potential applications of quantum machine learning to data analysis tasks in HEP and how to overcome the limitations of algorithms targeted for Noisy Intermediate-Scale Quantum (NISQ) devices.
Quantum computing and machine learning (ML) are converging to form a new paradigm in computational science, offering transformative capabilities in data processing, optimization, and pattern recognition. Quantum systems leveraging superposition, entanglement, and parallelism enable theoretical speedups for complex ML tasks that challenge classical architectures. Emerging quantum algorithms, such as quantum support vector machines (QSVMs), quantum principal component analysis (QPCA), and variational quantum circuits (VQCs), show promise for addressing high-dimensional, computationally intensive problems. Despite current limitations posed by noisy qubits, limited coherence times, and data encoding constraints, progress is accelerating. Hybrid quantum–classical models, noise-resilient techniques, and expanding quantum cloud infrastructures are enhancing the practical reach of quantum machine learning (QML). Applications in molecular modeling, optimization, and cybersecurity are among the early areas showing tangible potential. With the eventual advent of fault–tolerant quantum processors, QML could enable breakthroughs across disciplines, from drug discovery and materials science to next-generation artificial intelligence (AI). As the volume and complexity of data continue to grow, QML is positioned to redefine the future of intelligent computation.