检索结果-内蒙古大学图书馆

A Systematic Mapping Study on quantum and quantum-inspired algorithms in Operations Research

ACM COMPUTING SURVEYS 2025年第3期57卷 1-35页

作者： Gomes, Claudio Fernandes, Joao paulo Falcao, Gabriel Kar, Soummya Tayur, Sridhar Univ Porto Fac Engn Dept Informat Engn Porto Portugal Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA Univ Porto Lab Inteligencia Artificial & Ciencia Comp Porto Portugal New York Univ Abu Dhabi Abu Dhabi U Arab Emirates Univ Coimbra Dept Elect & Comp Engn Coimbra Portugal Inst Telecomunicacoes Coimbra Coimbra Portugal Carnegie Mellon Univ Tepper Sch Business Quantum Technol Grp Pittsburgh PA USA

quantum and quantum-inspired algorithms have not yet been systematically classified in the context of potential Operations Research (OR) applications. Our systematic mapping is designed for quick consultation and shows which algorithms have been significantly explored in the context of OR, as well as which algorithms have been vaguely addressed in the same context. The study provides rapid access to OR professionals, both practitioners and researchers, who are interested in applying and/or further developing these algorithms in their respective contexts. We prepared a replicable protocol as a backbone of this systematic mapping study, specifying research questions, establishing effective search and selection methods, defining quality metrics for assessment, and guiding the analysis of the selected studies. A total of more than 2,000 studies were found, of which 149 were analyzed in detail. Readers can have an interactive hands-on experience with the collected

关键词： quantum algorithms quantum-inspired algorithms operations research systematic mapping study

来源：评论

学校读者我要写书评

暂无评论

Evolutionary algorithms and quantum Computing: Recent Advances, Opportunities, and Challenges

引用

IEEE ACCESS 2025年 13卷 16649-16670页

作者： Ur Rehman, Junaid Ulum, Muhammad Shohibul Shaffar, Abdurrahman Wachid Hakim, Amirul Adlil Mujirin, Zaid Abdullah, Zaid Al-Hraishawi, Hayder Chatzinotas, Symeon Shin, Hyundong King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1855 Luxembourg City Luxembourg King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia Kyung Hee Univ Dept Elect & Informat Convergence Engn Yongin 17104 Gyeonggi Do South Korea Univ S Florida Dept Elect Engn Tampa FL 33620 USA

quantum computers have made significant progress in the last two decades showing great potential in tackling some of the most challenging problems in computing. This ongoing progress creates an opportunity to implement and evaluate quantum-inspired metaheuristics on real quantum devices, with the aim of uncovering potential computational advantages. Additionally, the practical constraints associated with current quantum computers have highlighted a critical need for classical heuristic methods to optimize the tunable parameters of quantum circuits. Nature-inspired metaheuristics have emerged as promising candidates for fulfilling this optimization role. In this paper, we discuss both of these potential directions at the intersection of evolutionary computing and quantum computing while surveying some of the most promising advancements in these directions. We start with the review of quantum-inspired metaheuristics and then explore implementations of some of these quantum-inspired algorithms on physical quantum devices, capitalizing on the progress in quantum computing technology. Furthermore, we investigate the role of nature-inspired metaheuristics in enhancing the performance of noisy intermediate-scale quantum computers by fine-tuning their parameters. Finally, we discuss some of the recent progress at the intersection of both computing frameworks to highlight the current status and potential of the currently available quantum computing hardware. Synergies between these two computing frameworks demonstrate the potential of a strongly symbiotic relation that can contribute to the simultaneous advancements in both of these computing paradigms.

关键词： quantum computing Evolutionary computation Qubit quantum mechanics Metaheuristics Heuristic algorithms Hardware Terminology Computers Recommender systems Evolutionary algorithms genetic algorithm quantum-inspired algorithms quantum computing

来源：评论

学校读者我要写书评

暂无评论

Machine learning with sub-diffraction resolution in the photon-counting regime

引用

quantum MACHINE INTELLIGENCE 2025年第1期7卷 1-12页

作者： Buonaiuto, Giuseppe Lupo, Cosmo Natl Res Council CNR Inst High Performance Comp & Networking ICAR I-80131 Naples Italy Univ & Politecn Bari Dipartimento Interateneo Fis I-70126 Bari Italy INFN Sez Bari I-70126 Bari Italy

The resolution of optical imaging is classically limited by the width of the point-spread function, which in turn is determined by the Rayleigh length. Recently, spatial-mode demultiplexing (SPADE) has been proposed as a method to achieve sub-Rayleigh estimation and discrimination of natural, incoherent sources. Here, we show that SPADE yields sub-diffraction resolution in the broader context of image classification. To achieve this goal, we outline a hybrid machine learning algorithm for image classification that includes a physical part and a computational part. The physical part implements a physical pre-processing of the optical field that cannot be simulated without essentially reducing the signal-to-noise ratio. In detail, a spatial-mode demultiplexer is used to sort the transverse field, followed by mode-wise photon detection. In the computational part, the collected data are fed into an artificial neural network for training and classification. As a case study, we classify images from the MNIST dataset after severe blurring due to diffraction. Our numerical experiments demonstrate the ability to classify highly blurred images that would be otherwise indistinguishable by direct imaging without the physical pre-processing of the optical field.

关键词： Super-resolution quantum-inspired algorithms Machine learning

来源：评论

学校读者我要写书评

暂无评论

quantum-inspired neural networks for time-series air pollution prediction and control of the most polluted region in the world

引用

quantum MACHINE INTELLIGENCE 2025年第1期7卷 1-13页

作者： Ahmad, Naushad Jas, Shubham Mahatma Gandhi Cent Univ Comp Sci & Informat Technol Motihari 845401 Bihar India

Researchers in the field of quantum computing are primarily focused on quantum-inspired algorithms, which function effectively on classical computers while incorporating quantum principles. A significant challenge in heavily polluted areas is accurately predicting air pollution levels. For our study, we selected New Delhi, specifically the Anand Vihar air pollution station, known for its high concentrations of PM2.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2.5}$$\end{document}. We proposed an approach to predict PM2.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{2.5}$$\end{document} levels based on various pollutant and meteorological parameters. Our model is an improved version of the quantum temporal convolutional network (QTCN), enhancing the traditional quantum convolutional neural network (QCNN) model. To evaluate our model's performance, we used several metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and the coefficient of determination (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} score). Our proposed model achieved MAE and MAPE values of (59.031 +/- 1.23\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$59.031 \pm 1.

关键词： quantum algorithms Dequantized algorithms quantum-inspired algorithms quantum convolutional neural network quantum temporal convolutional network

来源：评论

学校读者我要写书评

暂无评论

A quantum mechanics-based algorithm for vessel segmentation in retinal images

引用

quantum INFORMATION PROCESSING 2016年第6期15卷 2303-2323页

作者： Youssry, Akram El-Rafei, Ahmed Elramly, Salwa Ain Shams Univ Fac Engn Elect & Commun Engn Dept Cairo Egypt Ain Shams Univ Fac Engn Engn Phys & Math Dept Cairo Egypt

Blood vessel segmentation is an important step in retinal image analysis. It is one of the steps required for computer-aided detection of ophthalmic diseases. In this paper, a novel quantum mechanics-based algorithm for retinal vessel segmentation is presented. The algorithm consists of three major steps. The first step is the preprocessing of the images to prepare the images for further processing. The second step is feature extraction where a set of four features is generated at each image pixel. These features are then combined using a nonlinear transformation for dimensionality reduction. The final step is applying a recently proposed quantum mechanics-based framework for image processing. In this step, pixels are mapped to quantum systems that are allowed to evolve from an initial state to a final state governed by Schrodinger's equation. The evolution is controlled by the Hamiltonian operator which is a function of the extracted features at each pixel. A measurement step is consequently performed to determine whether the pixel belongs to vessel or non-vessel classes. Many functional forms of the Hamiltonian are proposed, and the best performing form was selected. The algorithm is tested on the publicly available DRIVE database. The average results for sensitivity, specificity, and accuracy are 80.29, 97.34, and 95.83 %, respectively. These results are compared to some recently published techniques showing the superior performance of the proposed method. Finally, the implementation of the algorithm on a quantum computer and the challenges facing this implementation are introduced.

关键词： quantum-inspired algorithms Blood vessel segmentation Biomedical signal and image processing Retinal image analysis

来源：评论

学校读者我要写书评

暂无评论

quantum-inspired Linear Genetic Programming as a Knowledge Management System

引用

COMPUTER JOURNAL 2013年第9期56卷 1043-1062页

作者： Dias, Douglas Mota Pacheco, Marco Aurelio C. Pontifical Catholic Univ Rio de Janeiro PUC Rio Dept Elect Engn Appl Computat Intelligence Lab ICA Rio De Janeiro Brazil

The superior performance of quantum computers in some problems lies in the direct use of quantum mechanics phenomena. This ability has originated the quantum-inspired evolutionary algorithms (QIEAs), which are classical algorithms (for classical computers) that exploit quantum mechanics principles to improve their performance. Several proposed QIEAs are able to outperform their traditional counterparts when applied to different kinds of problems. Aiming to exploit this new paradigm on genetic programming (GP), this paper introduces a novel QIEA model (quantum-inspired linear GP-QuaLiGP), which evolves machine code programs. QuaLiGP is inspired on multi-level quantum systems, and its operation is based on quantum individuals, which represent a superposition of all programs (solutions) of the search space. The tests use symbolic regression and binary classification as knowledge management problems to assess the QuaLiGP performance and compare it with Automatic Induction of Machine Code by Genetic Programming model, which is currently the most efficient GP model to evolve machine code. Results show that QuaLiGP outperforms the reference GP system for all these problems, by achieving better solutions from a smaller number of evaluations and by using fewer parameters and operators. This paper concludes that the quantum-inspired paradigm can be a competitive approach to evolve programs efficiently, encouraging improvements and extensions of QuaLiGP.

关键词： evolutionary computation genetic programming quantum-inspired algorithms symbolic regression binary classification

来源：评论

学校读者我要写书评

暂无评论

A quantum mechanics-based framework for image processing and its application to image segmentation

引用

quantum INFORMATION PROCESSING 2015年第10期14卷 3613-3638页

作者： Youssry, Akram El-Rafei, Ahmed Elramly, Salwa Ain Shams Univ Fac Engn Elect & Commun Engn Dept Cairo Egypt Ain Shams Univ Fac Engn Engn Phys & Math Dept Cairo Egypt

quantum mechanics provides the physical laws governing microscopic systems. A novel and generic framework based on quantum mechanics for image processing is proposed in this paper. The basic idea is to map each image element to a quantum system. This enables the utilization of the quantum mechanics powerful theory in solving image processing problems. The initial states of the image elements are evolved to the final states, controlled by an external force derived from the image features. The final states can be designed to correspond to the class of the element providing solutions to image segmentation, object recognition, and image classification problems. In this work, the formulation of the framework for a single-object segmentation problem is developed. The proposed algorithm based on this framework consists of four major steps. The first step is designing and estimating the operator that controls the evolution process from image features. The states associated with the pixels of the image are initialized in the second step. In the third step, the system is evolved. Finally, a measurement is performed to determine the output. The presented algorithm is tested on noiseless and noisy synthetic images as well as natural images. The average of the obtained results is 98.5 % for sensitivity and 99.7 % for specificity. A comparison with other segmentation algorithms is performed showing the superior performance of the proposed method. The application of the introduced quantum-based framework to image segmentation demonstrates high efficiency in handling different types of images. Moreover, it can be extended to multi-object segmentation and utilized in other applications in the fields of signal and image processing.

关键词： quantum-inspired algorithms Image processing Image segmentation Signal processing

来源：评论

学校读者我要写书评

暂无评论

Use of graphics processing units for automatic synthesis of programs

引用

COMPUTERS & ELECTRICAL ENGINEERING 2015年 46卷 112-122页

作者： da Silva, Cleomar Pereira Dias, Douglas Mota Bentes, Cristiana Cavalcanti Pacheco, Marco Aurelio Pontifical Catholic Univ Rio de Janeiro Dept Elect Engn Rio De Janeiro Brazil Fed Inst Educ Sci & Technol Catarinense IFC Florianopolis SC Brazil Univ Estado Rio De Janeiro Dept Syst Engn Rio De Janeiro Brazil

Genetic programming (GP) is an evolutionary method that allows computers to solve problems automatically. However, the computational power required for the evaluation of billions of programs imposes a serious limitation on the problem size. This work focuses on accelerating GP to support the synthesis of large problems. This is done by completely exploiting the highly parallel environment of graphics processing units (GPUs). Here, we propose a new quantum-inspired linear GP approach that implements all the GP steps in the GPU and provides the following: (1) significant performance improvements in the GP steps, (2) elimination of the overhead of copying the fitness results from the GPU to the CPU, and (3) incorporation of a new selection mechanism-to recognize the programs with the best evaluations. The proposed approach outperforms the previous approach for large-scale synthetic and real-world problems. Further, it provides a remarkable speedup over the CPU execution. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Genetic programming GPU acceleration Machine code quantum-inspired algorithms Massive parallelism

来源：评论

学校读者我要写书评

暂无评论

Hybrid quantum genetic algorithm with adaptive rotation angle for the 0-1 Knapsack problem in the IBM Qiskit simulator

引用

SOFT COMPUTING 2023年第18期27卷 13321-13346页

作者： Ballinas, Enrique Montiel, Oscar Inst Politecn Nacl CITEDI 1310 Ave Inst Politecn Nacl Tijuana 22435 Baja California Mexico

A Hybrid quantum Genetic Algorithm with an Adaptive Rotation Angle (HQGAAA) for the 0-1 knapsack problem is presented. This novel proposal uses the Deutsch-Jozsa quantum circuit to generate quantum populations, which synergistically works as haploid recombination and mutation operators taking advantage of quantum entanglement providing exploitative and explorative features to produce new individuals. Furthermore, the created individuals are updated using an adaptive rotation angle operator that helps refine new individuals to converge to the optimal solution. We performed comparative tests with other quantum evolutionary algorithms and the classical genetic algorithm to demonstrate that this proposal performed better with the tested problem. Results showed that quantum algorithms performed similar but better than the classic genetic algorithm regarding accuracy. Moreover, statistic tests demonstrated that our proposal is faster than the other quantum algorithms tested.

关键词： quantum-inspired algorithms Adaptive rotation angle Knapsack problem

来源：评论

学校读者我要写书评

暂无评论

A review of recent advances in quantum-inspired metaheuristics

引用

EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 627-642页

作者： Hakemi, Shahin Houshmand, Mahboobeh KheirKhah, Esmaeil Hosseini, Seyyed Abed Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Razavi Khorasan Iran Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Razavi Khorasan Iran

quantum-inspired metaheuristics emerged by combining the quantum mechanics principles with the metaheuristic algorithms concepts. These algorithms extend the diversity of the population, which is a primary key to proper global search and is guaranteed using the quantum bits' probabilistic representation. In this work, we aim to review recent quantum-inspired metaheuristics and to cover the merits of linking the quantum mechanics notions with optimization techniques and its multiplicity of applications in real-world problems and industry. Moreover, we reported the improvements and modifications of proposed algorithms and identified the scope's challenges. We gathered proposed algorithms of this scope between 2017 and 2022 and classified them based on the sources of inspiration. The source of inspiration for most quantum-inspired metaheuristics are the Genetic and Evolutionary algorithms, followed by swarm-based algorithms, and applications range from image processing to computer networks and even multidisciplinary fields such as flight control and structural design. The promising results of quantum-inspired metaheuristics give hope that more conventional algorithms can be combined with quantum mechanics principles in the future to tackle optimization problems in numerous disciplines.

关键词： quantum-inspired algorithms quantum computing Metaheuristics Optimization techniques Global optimization NP-hard problems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：