: Quantum physics-based quantum computing has gained attention as a new computing paradigm. As the quantum technology is advancing the Quantum Machine Learning (QML) is emerging as a new research area that combines quantum computing and machine learning. As classical machine learning continues to advance rapidly, by exploring new computing paradigms quantum machine learning is also progressing by taking a new parallel direction. In this paper, we implement and compare classical machine learning model, hybrid quantum-classical model, and fully quantum regression models for predicting Atmospheric Pollutant Concentration (APC). Multiple Linear Regression and Support Vector Regression are used as standard classical methods. Two hybrid models- Quantum Kernel Regression and Hybrid Quantum–Classical Regression, and a fully quantum model -Variational Quantum Regression are examined to understand how quantum techniques perform on today’s noisy quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ) devices. The implementation and testing are conducted using a real time atmospheric pollutant concentration dataset containing data from different states of India, which is collected from Open Government Data (OGD) platform. The quantum models are implemented using quantum circuits and simulated using IBM Qiskit. The studies show that in this NISQ era- mainly limited qubits which are noisy and limited depth circuits, the classical regression models give more accurate predictions than quantum models. However, the quantum and hybrid approaches show comparable training times and provide useful insights into how these methods may scale in the future. Overall, this study highlights both the potential and the current limitations of quantum regression techniques when applied to real-world environmental prediction tasks in the NISQ era.
: Quantum physics-based quantum computing has gained attention as a new computing paradigm. As the quantum technology is advancing the Quantum Machine Learning (QML) is emerging as a new research area that combines quantum computing and machine learning. As classical machine learning continues to advance rapidly, by exploring new computing paradigms quantum machine learning is also progressing by taking a new parallel direction. In this paper, we implement and compare classical machine learning model, hybrid quantum-classical model, and fully quantum regression models for predicting Atmospheric Pollutant Concentration (APC). Multiple Linear Regression and Support Vector Regression are used as standard classical methods. Two hybrid models- Quantum Kernel Regression and Hybrid Quantum–Classical Regression, and a fully quantum model -Variational Quantum Regression are examined to understand how quantum techniques perform on today’s noisy quantum computers, known as Noisy Intermediate-Scale Quantum (NISQ) devices. The implementation and testing are conducted using a real time atmospheric pollutant concentration dataset containing data from different states of India, which is collected from Open Government Data (OGD) platform. The quantum models are implemented using quantum circuits and simulated using IBM Qiskit. The studies show that in this NISQ era- mainly limited qubits which are noisy and limited depth circuits, the classical regression models give more accurate predictions than quantum models. However, the quantum and hybrid approaches show comparable training times and provide useful insights into how these methods may scale in the future. Overall, this study highlights both the potential and the current limitations of quantum regression techniques when applied to real-world environmental prediction tasks in the NISQ era.
Quantum machine learning has emerged as a promising approach to enhance computational efficiency by leveraging the principles of quantum computing. However, the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, such as noise, limited qubit availability, and circuit depth constraints, restrict the implementation of fully quantum models. To address these challenges, this study focuses on the design, implementation, and performance evaluation of hybrid quantum-classical machine learning (HQML) models. The proposed approach integrates parameterized quantum circuits with classical optimization techniques to enable efficient learning within NISQ environments. The study employs standard benchmark datasets, including Iris, Breast Cancer, and MNIST, to evaluate the performance of the hybrid model. The results indicate that the HQML model achieves competitive accuracy on small and medium-sized datasets while maintaining balanced precision, recall, and F1-score. However, performance declines for complex datasets due to hardware limitations and noise effects. Additionally, the hybrid model demonstrates a lower number of parameters compared to classical deep learning models but requires higher training time due to iterative quantum-classical optimization. The findings highlight that hybrid quantum-classical models provide a practical and scalable approach for utilizing quantum computing in the current technological landscape. Although challenges related to noise, scalability, and computational overhead persist, advancements in quantum hardware and algorithm design are expected to improve performance. This study contributes to the growing field of quantum machine learning by providing a systematic framework for evaluating hybrid models on NISQ devices and identifying key areas for future research.
Quantum Support Vector Regression (QSVR) has emerged as one of the most promising quantum machine learning algorithms for nonlinear regression on Noisy Intermediate-Scale Quantum (NISQ) devices. However, despite numerous simulation-based studies, experimental evaluations on physical quantum hardware remain relatively scarce. This work presents a comparative analysis of three regression approaches: classical Support Vector Regression (SVR) with a Radial Basis Function (RBF) kernel, classical SVR utilizing precomputed quantum kernels, and the native QSVR implementation available in the qiskit-machine-learning framework. Experiments were conducted on three benchmark regression datasets using three execution environments: a noiseless Qiskit Aer simulator, the IQM Adonis hardware-aware simulator, and the physical 5-qubit IQM Spark superconducting quantum computer (Odra 5) located at Wrocław University of Science and Technology. Multiple quantum feature maps and circuit depths were investigated, while model hyperparameters were optimized using grid search. The obtained results demonstrate that QSVR can be successfully executed on contemporary superconducting quantum hardware and, for selected datasets, achieves prediction accuracy comparable to or exceeding that of a classical RBF-based SVR. The experiments further reveal the significant influence of quantum hardware noise, feature map selection, and circuit depth on regression quality. Moreover, the implementation based on precomputed quantum kernels generally exhibits greater robustness on physical hardware than the native QSVR implementation. The presented benchmark provides an experimental assessment of QSVR under realistic execution conditions and contributes to the ongoing evaluation of quantum machine learning algorithms on current NISQ-era quantum computers.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Quantum computing has emerged as a transformative paradigm capable of addressing computational challenges beyond the reach of classical systems.In the current era of Noisy Intermediate-Scale Quantum (NISQ) devices, practical quantum hardware is characterized by limited qubit counts, short coherence times, gate errors, and the absence of full error correction.Despite these constraints, significant progress has been made in developing quantum machine learning (QML) techniques tailored to operate on near-term quantum processors.This paper presents a comprehensive review of Quantum Machine Learning in the context of NISQ computing.We first outline the fundamental principles of quantum computing, including qubits, superposition, entanglement, and quantum parallelism, followed by a detailed discussion of the NISQ computing model and its hardware limitations.The review then examines hybrid quantum-classical learning frameworks, variational quantum algorithms, quantum data encoding strategies, and noise mitigation techniques that enable practical implementation on existing platforms.Emerging applications across healthcare, finance, cybersecurity, climate modeling, and materials science are also surveyed to highlight the real-world potential of QML.Furthermore, key challenges such as scalability, trainability issues, and algorithmic robustness are analyzed, along with current research efforts aimed at overcoming these barriers.The paper concludes by identifying promising future directions for advancing QML toward quantum advantage.This review aims to provide researchers and practitioners with a structured understanding of the capabilities, limitations, and opportunities of quantum machine learning in the NISQ era .
Quantum Machine Learning (QML):An emerging research area that integrates principles of quantum computing with machine learning algorithms to enhance computational efficiency, scalability, and learning capability for complex data-driven problems. Cloud-Based Quantum Computing:A computing paradigm that provides remote access to quantum processors and simulators through cloud platforms, enabling scalable, cost-effective, and accessible quantum experimentation without the need for dedicated quantum hardware. Hybrid Quantum–Classical Models:Machine learning architectures that combine classical data preprocessing and optimization techniques with quantum circuits to leverage the strengths of both classical and quantum computation. Weather Forecasting:The process of predicting atmospheric conditions such as temperature, humidity, pressure, and wind speed using data-driven models and computational techniques. Variational Quantum Circuits (VQC):Parameterized quantum circuits optimized using classical algorithms, widely used in quantum machine learning for classification and regression tasks on Noisy Intermediate-Scale Quantum (NISQ) devices. NISQ Devices:Current-generation quantum computers characterized by limited qubit counts and noise, which influence the performance and accuracy of present-day quantum algorithms.
Hybrid quantum-classical machine learning (HQML) models represent an emerging computational paradigm that combines the strengths of quantum computing and classical artificial intelligence to solve complex learning problems more efficiently.Quantum computing offers massive parallelism, superposition, and entanglement, enabling computational capabilities beyond classical systems for selected tasks.However, current quantum hardware remains limited by noise, decoherence, and scalability constraints.Hybrid architectures overcome these limitations by integrating quantum processors with classical machine learning frameworks, allowing practical implementation on Noisy Intermediate-Scale Quantum (NISQ) devices.This review paper presents a comprehensive study of hybrid quantum-classical machine learning models, including their architecture, working principles, quantum circuits, optimization techniques, and applications in healthcare, cybersecurity, finance, robotics, and big data analytics.The paper also discusses quantum neural networks, variational quantum circuits, quantum support vector machines, and quantum generative models.Furthermore, comparative analysis between classical and hybrid quantum systems is presented to evaluate performance, scalability, computational efficiency, and implementation challenges.Major issues such as quantum noise, hardware limitations, algorithm complexity, and high operational cost are examined in detail.Finally, future research directions and emerging trends in hybrid quantum-classical computing are discussed.The review concludes that HQML models have the potential to revolutionize next-generation intelligent computing systems.