This research endeavor centers on augmenting the quantum circuitry of the Quantum Support Vector Machine (QSVM), a pivotal algorithm in the realm of quantum machine learning, via a hybrid optimization methodology that amalgamates Quantum Natural Gradient Descent (QNGD) with the Whale Optimization Algorithm (WOA). QSVM harnesses the power of quantum computing to facilitate proficient classification within high-dimensional spaces; however, its efficacy is frequently compromised by suboptimal circuit parameters and quantum noise prevalent in near-term quantum devices. The proposed hybrid optimization paradigm integrates the global search proficiency of WOA with the effective parameter refinement capabilities of QNGD, culminating in expedited convergence (38 iterations), enhanced classification accuracy (96.8%), optimized circuit complexity (44 quantum gates), and increased resilience against noise (fidelity: 0.985). In comparison to conventional optimization techniques such as Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA), our methodology demonstrates superior efficacy by refining quantum kernel computation, entanglement configurations, and circuit depth, while concurrently alleviating the impacts of quantum noise.
This research endeavor centers on augmenting the quantum circuitry of the Quantum Support Vector Machine (QSVM), a pivotal algorithm in the realm of quantum machine learning, via a hybrid optimization methodology that amalgamates Quantum Natural Gradient Descent (QNGD) with the Whale Optimization Algorithm (WOA). QSVM harnesses the power of quantum computing to facilitate proficient classification within high-dimensional spaces; however, its efficacy is frequently compromised by suboptimal circuit parameters and quantum noise prevalent in near-term quantum devices. The proposed hybrid optimization paradigm integrates the global search proficiency of WOA with the effective parameter refinement capabilities of QNGD, culminating in expedited convergence (38 iterations), enhanced classification accuracy (96.8%), optimized circuit complexity (44 quantum gates), and increased resilience against noise (fidelity: 0.985). In comparison to conventional optimization techniques such as Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA), our methodology demonstrates superior efficacy by refining quantum kernel computation, entanglement configurations, and circuit depth, while concurrently alleviating the impacts of quantum noise.
This research endeavor is centered on the enhancement of the quantum circuit associated with the Quantum Support Vector Machine (QSVM), a fundamental algorithm within the realm of quantum machine learning, through the application of a hybrid optimization methodology that amalgamates Quantum Natural Gradient Descent (QNGD) with the Quantum Butterfly Optimization Algorithm (QBOA). The QSVM exploits quantum computational capabilities for proficient classification tasks within elevated-dimensional frameworks; however, its operational efficacy is frequently compromised by non-ideal quantum circuit parameters and the deleterious effects of noise present in near-term quantum computational devices. The suggested hybrid optimization technique augments the performance of QSVM by fusing the localized refinement capabilities of QNGD, which employs the Fisher Information Matrix for effective parameter adjustments, with the expansive exploration potential of QBOA, inspired by the foraging behaviors of butterflies. This collaborative approach yields accelerated convergence (38 iterations), enhanced classification accuracy (96.8%), diminished circuit complexity (44 quantum gates), and increased resilience to noise (fidelity:0.985). In comparison to traditional optimization techniques such as Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Firefly Algorithm (FA), the proposed framework exhibits markedly superior efficacy in the refinement of quantum kernel computations, entanglement strategies, and the overall depth of the circuit while concurrently alleviating the impacts of noise.
This study concentrates on the enhancement of the quantum circuit pertaining to Quantum K-Nearest Neighbors (QKNN), which is a fundamental algorithm within the domain of quantum machine learning, through the implementation of a hybrid optimization methodology that integrates Gradient Descent with the Golden Eagle Optimization Algorithm (GEOA). QKNN harnesses the capabilities of quantum computing to process high-dimensional datasets with enhanced computational efficiency and precision; however, its effectiveness is frequently compromised by inadequate quantum circuit parameters and elevated noise levels present in near-term quantum devices. The proposed hybrid optimization methodology augments Quantum K-Nearest Neighbors (QKNN) through the amalgamation of the global search capabilities inherent in Golden Eagle Optimization (GEO) and the localized refinement efficacy provided by Gradient Descent (GD). This collaborative interplay culminates in expedited convergence (40 iterations), elevated classification accuracy (97.2 %), diminished circuit complexity (42 quantum gates), and enhanced resilience against gate noise (fidelity: 0.983). In contrast to conventional optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), our proposed methodology demonstrates superior efficacy by optimizing quantum distance computation, entanglement strategies, and circuit depth, while simultaneously attenuating the effects of gate noise.
This research centers on enhancing quantum circuits for Quantum Decision Trees (QDT) a highly promising algorithm in the realm of quantum machine learning aimed at classification and pattern recognition-via a hybrid metaheuristic optimization framework that integrates Golden Eagle Optimization (GEO) with Adaptive Gradient Descent (AGD). Although QDTs provide interpretability and quantum-enhanced classification capabilities for high-dimensional data sets, their effectiveness is frequently limited by subpar circuit configurations, excessive gate depth, and susceptibility to quantum noise. The suggested hybrid approach utilizes the global search capability of GEO and the local convergence accuracy of AGD to optimize QDT parameters such as entanglement structure, placement of quantum gates, and decision thresholds. Experimental findings indicate that the proposed technique achieves quicker convergence (38 iterations), superior classification accuracy (97.8 %), decreased quantum circuit complexity (39 quantum gates), and enhanced noise resilience (fidelity: 0.986), surpassing traditional methods including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). These results affirm the promise of hybrid metaheuristic optimizers in improving quantum circuit efficiency for real-world quantum machine learning applications.
Quantum Machine Learning (QML) has emerged as a paradigm that combines the computational advantages of quantum computing with the predictive capabilities of machine learning to address complex data classification problems. As data dimensionality increases rapidly and classical learning algorithms face scalability constraints, QML leverages quantum parallelism, entanglement, and high-dimensional Hilbert space representations to enhance learning performance. This paper reviews and analyses advanced QML algorithms, including Quantum Support Vector Machines (QSVM), Variational Quantum Circuits (VQC), Quantum Neural Networks (QNN), and Quantum Kernel Estimation, for optimizing both binary and multi-class classification tasks under high-complexity conditions. The proposed QML framework is evaluated on benchmark datasets like MNIST, the Breast Cancer Wisconsin (BCW) dataset, and synthetic nonlinear datasets, and is compared against classical machine learning baselines, including Support Vector Machines (SVM), Random Forests (RF), and Deep Neural Networks (DNN). The results demonstrate notable improvements in classification accuracy (up to 96.8%), decision margins, and computational efficiency in quantum-suitable data regimes, while also highlighting current limitations related to noise, circuit depth, and hardware constraints. Overall, the study presents a unified QML framework, theoretical formulations, and experimental evaluations that illustrate the potential of quantum algorithms for next-generation classification tasks.
Abstract Quantum Machine Learning (QML) represents a transformative paradigm that leverages quantum mechanical principles to enhance computational optimization in complex systems beyond the capabilities of classical machine learning algorithms. This paper investigates the optimization advantages of QML through a comprehensive analysis of quantum neural networks, quantum support vector machines, and hybrid quantum-classical architectures applied to complex system optimization problems. Our methodology employs variational quantum circuits with optimized feature encoding strategies and compares performance against classical baselines across multiple complexity scales. Experimental results demonstrate that QML algorithms achieve superior accuracy (95.1% for hybrid approaches vs 89.5% for classical neural networks), faster convergence rates (10x improvement in optimization iterations), and exponential scalability advantages for large-scale problems (31.7x speedup for problems with 5000 + parameters). The findings reveal that quantum algorithms excel in exploring high-dimensional solution spaces through superposition and entanglement, enabling more efficient navigation of complex optimization landscapes. Key contributions include a novel hybrid quantum-classical optimization framework, systematic performance benchmarking across problem complexities, and identification of quantum advantage thresholds for practical deployment. Results indicate that QML provides significant computational benefits for complex system optimization, particularly in scenarios involving large parameter spaces, non-convex optimization landscapes, and real-time processing requirements.
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization.