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 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.
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.
Abstract Text classification, a key process in natural language processing (NLP), relies on the k‐nearest neighbors (KNN) algorithm for its simplicity and effectiveness. Traditional methods often grapple with the high‐dimensional nature of textual data, leading to substantial computational demands. This study introduces a novel classical quantum k‐nearest neighbors (CQKNN) algorithm, which integrates quantum circuits into a conventional machine‐learning framework to enhance computational efficiency and reduce storage requirements. This hybrid approach uses a unified quantum circuit that simplifies multiple similarity calculations through mid‐circuit measurements and qubit reset operations, significantly improving upon traditional multi‐circuit quantum k‐nearest neighbors (QKNN) models. The CQKNN algorithm, tested on datasets such as SMS Spam Collection, Twitter US Airline Sentiment, and IMDB Movie Reviews, not only outperforms classical KNN but also addresses challenges posed by noisy intermediate‐scale quantum (NISQ) devices through advanced error mitigation techniques. This work highlights resource efficiency and reduced gate complexity and demonstrates the practical application of fidelity in quantum similarity calculations, setting new standards for quantum‐enhanced machine learning and advancing current quantum technology capabilities in complex data classification tasks.
Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high dimensional state or function approximation. Quantum-Classical hybrid algorithms are proposed in recent years for this purpose under the Noisy-Intermediate Scale Quantum computer (NISQ) environment. Under this scheme, the role played by classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presents the same or even higher test accuracy.
Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high-dimensional state or function approximation. Quantum–classical hybrid algorithms have been proposed in recent years for this purpose under the noisy intermediate-scale quantum computer (NISQ) environment. Under this scheme, the role played by the classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent-based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presenting the same or even higher test accuracy. Meanwhile, the proposed theoretical scheme was successfully implemented on the 20-qubit quantum computer of IBM Q, ibmq_johannesburg. The experimental results reveal that the gate error, especially the CNOT gate error, strongly affects the derived gradient accuracy. The regression accuracy performed on the IBM Q becomes lower with the increase in the number of measurement shot times due to the accumulated gate noise error.