The demand for extensive field data for remote sensing applications has created bottlenecks for classical machine learning algorithms. This paper presents a quantum machine learning model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA Hyperspectral Imagery. The performance of the developed quantum machine learning model is validated in terms of dataset size, overall accuracy, number of qubits, training, and predicting speed. The results showed that QSVM outperformed Classic SVM by 5% accuracy with 50 samples at 12 qubits/feature or higher and 20 samples at 16 qubits/feature. QSVM training times were 284 seconds for 50 samples and 54 seconds for 20 samples, while prediction times for 400 pixels were 5243 seconds (50-sample model) and 2845 seconds (20-sample model). Higher accuracy achieved by QML exhibits great potential for limited data studies, with a drawback of slow training/testing speed. Another observation is that the sample size influences QSVM prediction speed in the training process. Therefore, we propose Quantum Machine Learning as a potential solution to tackle ground truth challenges as it can learn from less data.
The demand for extensive field data for remote sensing applications has created bottlenecks for classical machine learning algorithms. This paper presents a quantum machine learning model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA Hyperspectral Imagery. The performance of the developed quantum machine learning model is validated in terms of dataset size, overall accuracy, number of qubits, training, and predicting speed. The results showed that QSVM outperformed Classic SVM by 5% accuracy with 50 samples at 12 qubits/feature or higher and 20 samples at 16 qubits/feature. QSVM training times were 284 seconds for 50 samples and 54 seconds for 20 samples, while prediction times for 400 pixels were 5243 seconds (50-sample model) and 2845 seconds (20-sample model). Higher accuracy achieved by QML exhibits great potential for limited data studies, with a drawback of slow training/testing speed. Another observation is that the sample size influences QSVM prediction speed in the training process. Therefore, we propose Quantum Machine Learning as a potential solution to tackle ground truth challenges as it can learn from less data.
Quantum machine learning (QML) focuses on machine learning models developed explicitly for quantum computers. Availability of the first quantum processor led to further research, particularly exploring possible practical applications of QML algorithms in the remote sensing field. The demand for extensive field data for remote sensing applications has started creating bottlenecks for classical machine learning algorithms. QML is becoming a potential solution to tackle big data problems as it can learn from fewer data. This paper presents a QML model based on a quantum support vector machine (QSVM) to classify Holm Oak trees using PRISMA hyperspectral Imagery. Implementation of quantum models was carried on a quantum simulator and a real-time superconducting quantum processor of IBM. The performance of the QML model is validated in terms of dataset size, overall accuracy, number of qubits, training and predicting speed. Results were indicative that (i) QSVM offered 5% higher accuracy than classical SVM (CSVM) with 50 samples and ≥12 qubits/feature dimensions whereas with 20 samples at 16 Qubits/feature dimension, (ii) training time for QSVM at maximum accuracy was 284 s with 50 samples and with 20 samples was 53.68 s and (iii) predicting time for 400 pixels using the QSVM model trained with 50 samples dataset was 5243 s whereas with 20 samples dataset was 2845 s. Results were indicative that QML offers better accuracy but lack training and predicting speed for hyperspectral data. Another observation is that predicting speed of QSVM depends on the number of samples used to train the model.
A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers. The availability of quantum processors has led to practical applications of QML algorithms in the remote sensing field. Quantum machines can learn from fewer data than non-quantum machines, but because of their low processing speed, quantum machines cannot be applied to an image that has hundreds of thousands of pixels. Researchers around the world are exploring applications for QML and in this work, it is applied for pseudo-labelling of samples. Here, a PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral dataset is prepared by quantum-based pseudo-labelling and 11 different machine learning algorithms viz., support vector machine (SVM), K-nearest neighbour (KNN), random forest (RF), light gradient boosting machine (LGBM), XGBoost, support vector classifier (SVC) + decision tree (DT), RF + SVC, RF + DT, XGBoost + SVC, XGBoost + DT, and XGBoost + RF with this dataset are evaluated. An accuracy of 86% was obtained for the classification of pine trees using the hybrid XGBoost + decision tree technique.
Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.
Quantum Machine Learning (QML) provides a pathway for analysing nonlinear relationships through quantum- enhanced feature spaces. Although quantum models are theoreti- cally well suited for high-complexity data, current hardware and simulator limitations restrict their use to small datasets. For this reason, the original large Web Phishing Detection Dataset was reduced to 200 representative samples through dimensionality reduction, allowing controlled experimentation and enabling evaluation of quantum behaviour with manageable computational cost. QML research commonly follows this approach, testing algorithms on small datasets first, because quantum feature maps can generate exponentially expressive Hilbert-space embeddings even from limited inputs. In this study, multiple Quantum Support Vector Machine (QSVM) models were implemented using fidelity-based quantum kernels and Pauli feature maps. Models with 2 to 10 qubits were evaluated to observe how circuit width affects performance. The two-qubit QSVM achieved the best quantum result with 72 % accuracy, whereas deeper circuits showed declining performance, indicating overfitting and reduced generalization on limited data. In contrast, the classical SVM attained 97 % accuracy with significantly lower computational effort. These findings highlight the challenges and potential of QML for cybersecurity tasks. Future work will explore circuit- expressivity tuning, optimized quantum kernels, hybrid quantum- classical approaches, and scalability to larger phishing datasets as quantum hardware evolves.
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.
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.