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Román Orús, Samuel Mugel, Enrique Lizaso • Reviews in Physics
(2019)
We discuss how quantum computation can be applied to financial problems, providing an overview of current approaches and potential prospects. We review quantum optimization algorithms , and expose how quantum annealers can be used to optimi...
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DOI: https://doi.org/10.1016/j.revip.2019.100028
Shuai Zhang, Ke-Xin Chen, Ji-Chong Yang • European Physical Journal C: Particles and Fields
(2025)
Abstract In recent years, with the increasing luminosities of colliders, handling the growing amount of data has become a major challenge for future New Physics (NP) phenomenological research. In order to improve efficiency, machine learnin...
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Open Access (DOAJ)
DOI: 10.1140/epjc/s10052-025-14069-1
Valeria Cimini, Mandar M. Sohoni, Federico Presutti, Benjamin K. Malia et al. • npj Quantum Information
(2026)
Abstract A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at a large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with > 400 modes as a quan...
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Open Access (DOAJ)
DOI: 10.1038/s41534-026-01251-w
Xiaoxuan Pan, Zhide Lu, Weiting Wang, Ziyue Hua et al. • Nature Communications
(2023)
Abstract Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report a...
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Open Access (DOAJ)
DOI: 10.1038/s41467-023-39785-8
Anup Basnet Chetry, Keisuke Ohto • Journal of Cheminformatics
(2025)
Abstract Chemoinformatics is a rapidly advancing field that integrates chemistry, computer science, and data analysis to enhance the study and application of chemical systems. This interdisciplinary approach leverages computational tools an...
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Open Access (DOAJ)
DOI: 10.1186/s13321-025-00978-6
Vikas Hassija, Vinay Chamola, Adit Goyal, Salil S. Kanhere et al. • IET Quantum Communication
(2020)
We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for ...
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DOI: https://doi.org/10.1049/iet-qtc.2020.0026
Thomas Spriggs, Arash Ahmadi, Bokai Chen, Eliska Greplova • Machine Learning: Science and Technology
(2025)
Variational techniques have long been at the heart of atomic, solid-state, and many-body physics. They have recently extended to quantum and classical machine learning, providing a basis for representing quantum states via neural networks. ...
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Open Access (DOAJ)
DOI: 10.1088/2632-2153/adaca2
Ronald de Wolf • arXiv (Cornell University)
(2019)
This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subse...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1907.09415
R Vinutha, V Haripriya • International Journal of Advanced Research in Science Communication and Technology
(2024)
In order to solve challenging optimization issues, analyze data effectively, and improve the capabilities of current machine learning algorithms, quantum computing has the potential to revolutionize the area of machine learning. The propose...
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Open Access
DOI: https://doi.org/10.48175/ijarsct-15955
Kiran Kumar Amireddy, P. V. R. Ravindra Reddy, C. Nagendranatha Reddy, P. Prabhakar Reddy • Frontiers in Sensors
(2026)
This review critically discusses the development of continuous monitoring biosensors and the technological developments that have facilitated the transition from one-off or periodic measurement sensors to longer-term, connected and autonomo...
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Open Access (DOAJ)
DOI: 10.3389/fsens.2026.1968999
Raviraju Balappa D, Gautam Kumar Rajput • Shodh Sari
(2025)
Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...
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Open Access (DOAJ)
DOI: https://doi.org/10.59231/SARI7840
Sayaki Matsushita • IEEE Transactions on Quantum Engineering
(2026)
Linear regression is fundamental to statistical analysis and machine learning, but its application to large-scale datasets necessitates distributed computing. The problem also arises in quantum computing, where handling extensive data requi...
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Open Access (DOAJ)
DOI: 10.1109/TQE.2026.3687237
Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang et al. • Frontiers in Immunology
(2025)
BackgroundM2-like tumor-associated macrophages (TAMs) promote an immunosuppressive microenvironment and contribute to tumor progression and metastasis. However, their molecular characterization and prognostic value have not been fully explo...
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Open Access (DOAJ)
DOI: 10.3389/fimmu.2025.1650726
Daniel Mills, Seyon Sivarajah, Travis L. Scholten, Ross Duncan • Quantum
(2021)
Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational ...
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Open Access (DOAJ)
DOI: 10.22331/q-2021-03-22-415
Stefano Mangini, Francesco Tacchino, Dario Gerace, Daniele Bajoni et al. • Europhysics Letters (EPL)
(2021)
Abstract Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale quantum computing devices have become available in rece...
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Open Access
DOI: https://doi.org/10.1209/0295-5075/134/10002
Md Habib Ullah, Rozhin Eskandarpour, Honghao Zheng, Amin Khodaei • IET Generation Transmission & Distribution
(2022)
Abstract Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that...
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DOI: https://doi.org/10.1049/gtd2.12602
Frederik F. Flöther • Research Directions Quantum Technologies
(2023)
Abstract Quantum computing hardware and software have made enormous strides over the last years1. Questions around quantum computing’s impact on research and society have changed from “if” to “when/how”. The 2020s have been described as the...
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DOI: https://doi.org/10.1017/qut.2023.4
Jeffery C. Atik, Valentin Jeutner • Law Innovation and Technology
(2019)
Quantum computing technology will greatly enhance the abilities of the emerging field of computational law to express, model, and operationalise law in algorithmic form. Foreshadowing the harnessing of the power of quantum computing technol...
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DOI: https://doi.org/10.1080/17579961.2021.1977216
Péter Wittek (2014)
Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that expla...
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Carlos Outeiral, Martin Strahm, Jiye Shi, Garrett M. Morris et al. • Wiley Interdisciplinary Reviews Computational Molecular Science
(2020)
Abstract Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all sci...
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DOI: https://doi.org/10.1002/wcms.1481