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.
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.
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 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.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
Adaptive quantum machine learning (QML) represents an exciting frontier at the intersection of quantum computing and classical bio-inspired optimization. The constraints of noisy intermediate-scale quantum (NISQ) devices—limited qubit counts, significant decoherence, and measurement uncertainties—pose formidable challenges to training parameterized quantum circuits (PQCs) using traditional gradient-based methods. To overcome these hurdles, we propose a novel Adaptive PSO–QML framework that integrates a particle swarm optimization (PSO)–inspired algorithm with dynamic hyperparameter adjustment, enabling real-time tuning of PQC parameters based on performance feedback. Unlike static optimizers, our approach continuously adapts its inertia weight and cognitive/social coefficients to balance exploration and exploitation, responding to stagnation or rapid improvement in measured fitness. We evaluate this hybrid algorithm on two benchmark classification tasks: the classical Iris dataset encoded into a 2-qubit circuit and a quantum-encoded subset of the MNIST dataset on 4 qubits. Our experiments, conducted on the Qiskit Aer simulator under realistic noise models (depolarizing, amplitude damping, and mixed), demonstrate that Adaptive PSO–QML achieves an average accuracy improvement of 12% over fixed-parameter PSO and 15% over Adam-based gradient descent, with p-values below 0.01 across paired t-tests. Statistical analysis summarized in Table 1 confirms the significance of these gains. Detailed simulation research further reveals that the adaptive mechanism not only accelerates convergence by approximately 30%—reducing total circuit evaluations—but also exhibits robust performance across varying circuit depths (1–3 entangling layers) and noise intensities. Our findings suggest that bio-inspired adaptation is a powerful tool for mitigating barren plateaus and noise effects in NISQ-era QML, paving the way for scalable quantum advantage in near-term hardware.
Although quantum computers will not necessarily transform ML in general, they hold immense potential for specific uses, such as dealing with high-dimensional data and complex correlations that cannot be effectively modeled using classical systems. It is, however, difficult to design efficient quantum circuits, especially with the existing Noisy Intermediate-Scale Quantum (NISQ) hardware that has constraints including decoherence, a limited number of qubits, and gate fidelity limitations. The authors propose an Adaptive Quantum Circuit Architecture Search (AQCAS) framework whose structure and parameters are optimized dynamically to improve the performance of the task-specific ML. The framework integrates quantum circuit encoding, a hybrid search strategy that is based on reinforcement learning and evolutionary algorithms, and a task-specific adaptivity mechanism. Benchmark datasets were used for experimental evaluations of both simulation using Qiskit Aer and classical optimization using the Python frameworks TensorFlow and PennyLane. Fixed and standard variational quantum circuits were compared with performance measurements like the classification accuracy, circuit depth, convergence speed, and computational efficiency. The results show that the accuracy of AQCAS can be raised to 94.1%, and the circuit depth is lowered at the same time. It converged rapidly and is computationally efficient. Ablation studies show that both hybrid search and adaptivity mechanisms help to enhance generalization and stability under NISQ constraints. The results demonstrate the potential of AQCAS as a practical approach to using adaptive quantum circuits in specialized ML applications, and lay the groundwork for future development of scaling to larger devices, implementing noise-aware optimization, and integrating with hybrid quantum-classical ML pipelines.