Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Delilbasic, Amer; Cavallaro, Gabriele; Willsch, Madita; Melgani, Farid; Riedel, Morris; Michielsen, Kristel

doi:10.1109/igarss47720.2021.9554802

Cited by 27 publications

(17 citation statements)

References 15 publications

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“…They observed a potential advantage of using an SVM with quantum annealing, over other classical approaches, for bank loan time series data. Delilbasic et al (2021) implemented two formulations of a quantum support vector machine (QSVM) using IBM quantum computers and D-Wave quantum annealers and compared the results for remote sensing (RS) images. Bhatia and Phillipson (2021) compared classical approach, simulated annealing, hybrid solver, and fully quantum implementations for public Banknote Authentication dataset and the Iris Dataset.…”

Section: Related Work With Cnns and Svmsmentioning

confidence: 99%

Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

Guddanti,

Padhye,

Prabhakar

et al. 2024

Front. Comput. Sci.

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Early diagnosis of pneumonia is crucial to increase the chances of survival and reduce the recovery time of the patient. Chest X-ray images, the most widely used method in practice, are challenging to classify. Our aim is to develop a machine learning tool that can accurately classify images as belonging to normal or infected individuals. A support vector machine (SVM) is attractive because binary classification can be represented as an optimization problem, in particular as a Quadratic Unconstrained Binary Optimization (QUBO) model, which, in turn, maps naturally to an Ising model, thereby making annealing—classical, quantum, and hybrid—an attractive approach to explore. In this study, we offer a comparison between different methods: (1) a classical state-of-the-art implementation of SVM (LibSVM); (2) solving SVM with a classical solver (Gurobi), with and without decomposition; (3) solving SVM with simulated annealing; (4) solving SVM with quantum annealing (D-Wave); and (5) solving SVM using Graver Augmented Multi-seed Algorithm (GAMA). GAMA is tried with several different numbers of Graver elements and a number of seeds using both simulating annealing and quantum annealing. We found that simulated annealing and GAMA (with simulated annealing) are comparable, provide accurate results quickly, competitive with LibSVM, and superior to Gurobi and quantum annealing.

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Section: Related Work With Cnns and Svmsmentioning

confidence: 99%

Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

Guddanti,

Padhye,

Prabhakar

et al. 2024

Front. Comput. Sci.

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“…In the field of RS, there are particular applications of QC that have been developed recently. For example, in [138]- [141], QML algorithms such as support vector machines (SVMs) and neural networks are applied for classification of multispectral images. In [142], the authors use a quantum annealer to perform the following three tasks on hyperspectral data: classification using a variant of SVMs, band selection for classification, and boosting of classical classifiers.…”

Section: Qcmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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T he High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

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“…More precisely, it is responsible for training the model parameters from a set of labeled training data to make correct guesses on the test data. These SVMs are known to have higher stability than decision trees or deep neural networks that perform the same role [64], [65]. Therefore, there is an advantage that small fluctuations made by some data in the training data do not have a large effect on the classification result.…”

Section: ) Support Vector Machinementioning

confidence: 99%

How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey

Heng

Kim

et al. 2022

IEEE Access

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A combinatorial optimization problem (COP) is the problem of finding the optimal solution in a finite set. When the size of the feasible solution set is large, the complexity of the problem increases, and it is not easy to solve in a reasonable time with the current classical computer technology. Quantum annealing (QA) is a method that replaces classical simulated annealing (SA) methods that do not solve these cases. Therefore, several attempts have been made to solve this problem using a special-purpose quantum annealer to which the QA method is applied. In this survey, we analyze recent studies that solve real-scale COPs using quantum annealers. Through this, we discuss how to reduce the size of the COP to be input to overcome the hardware limitations of the existing quantum annealer. Additionally, we demonstrated the applicability of quantum annealer to COP on a practical scale by comparing and analyzing the results of the classical simulated annealing (SA) and QA method from each study. INDEX TERMSCombinatorial optimization problem (COP), Simulated annealing (SA), Quantum annealing (QA), Real quantum machine

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Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification

Cited by 27 publications

References 15 publications

Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

Pneumonia detection by binary classification: classical, quantum, and hybrid approaches for support vector machine (SVM)

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey

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