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Scientific Reports

An enhanced approach for predicting air pollution using quantum support vector machine

Omer Farooq, Maida Shahid, Shazia Arshad, Ayesha Altaf, Faiza Iqbal, Yini Airet Miró Vera, Miguel Ángel López Flores, Imran Ashraf

Published Aug 22, 2024• 38 citations
Publisher

Abstract

The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.

DOI
10.1038/s41598-024-69663-2

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The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM's quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM's state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.
Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks. Through a comparative analysis, we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality. As a case study, we explored the potential of quantum paradigms in the context of a real-world problem: classifying pancreatic cancer biomarker data. The Support Vector Classifier (SVC) algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework. The classical approach as well as the quantum-based techniques reported similar accuracy. This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset. Remarkably, quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency. This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning.
Abstract The study proposes a mechanism to generate effective feature maps with optimal feature selection using the Tabu Search algorithm. It compares the performance of classical support vector machines (SVM), quantum support vector machines (QSVM) with only gate selection, and QSVM with both gate selection and feature selection (QSVM-FS) across various datasets. The results indicate that classical SVMs excel with several benchmark datasets, while QSVMs show superior performance on synthetic datasets with non-linear separability. Notably, QSVM-FS consistently outperforms QSVM without feature selection, highlighting the importance of feature selection in enhancing model accuracy. These findings suggest that while both quantum and classical SVMs have unique advantages, quantum methods offer particular benefits in specific scenarios. In the NISQ era, classical simulations are a primary tool for assessing quantum experiments, though they face challenges such as design impacts, limited scales, and biases. Ultimately, no definitive winner exists between quantum and classical methods, as both have their own strengths.
Crop recommendation systems are very important in sustainable agriculture and yield optimization. Most of the traditional machine learning algorithms, such as SVM, do not work well on datasets for which there is a strong nonlinear relationship between the features such as soil parameters, climatic conditions and labels of crops. In this paper, we investigate the performance difference between classical SVM and Quantum Support Vector Machine (QSVM) when trained with highly complex, non-linear agriculture dataset. A synthetic non-linear dataset was constructed to mimic challenging real-world agriculture patterns, and is used to evaluate both the models fairly. Experimental results shows that classical SVM struggles to model deep feature dependencies, while QSVM significantly outperforms it by 10% increase in the accuracy due to the advantage of quantum feature encoding and high-dimensional Hilbert space mapping. This paper illustrates the potential benefits of quantum machine learning in different agricultural applications and indicates the advantages of quantum-enhanced models when dealing with challenging data sets.
The Support Vector Machine (SVM) is a well-known supervised machine learning approach aimed at facing classification problems. Thanks to the exploitation of kernel functions, SVM succeeds to address also complex classification tasks involving non-linearly separable data. However, there are limitations to its success when the feature space becomes large, and the kernel functions become computationally expensive to estimate. Quantum-enhanced Support Vector Machine (QSVM) utilizes the properties of quantum computers to obtain an exponential speed up with respect to the conventional SVM by using the quantum state space as the feature space. Unfortunately, to benefit from these advantages, currently, it is necessary to have a background knowledge about quantum computing concepts and specific language skills. The goal of this paper is to introduce a web-based tool to make simple to all researchers, coming from several and heterogeneous scientific backgrounds, the application of QSVM to different real-world classification problems. To achieve this goal, the presented web tool involves data preprocessing techniques and a user-friendly interface. The suitability of the presented web tool is shown in an experimental session where QSVM is applied to solve well-known classification tasks.
Quantum Support Vector Machine (Quantum SVM) is a prominent algorithm in quantum machine learning. A key challenge in this field is the efficient mapping of classical data into a quantum feature space. Despite its significance, there is a lack of systematic studies identifying the most suitable feature map for remote sensing specfic classification tasks. This study bridges that gap by determining the optimal feature map for forest class identification, revealing the superior potential of the Pauli feature map in remote sensing data classification. By leveraging Pauli feature representations, Quantum SVMs enable more effective extraction of quantum-enhanced patterns from complex datasets, providing a robust framework for solving non-linear classification problems in quantum machine learning.