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International Conference Communication, Networks & Computing

Application of Quantum Heuristic Optimization Algorithm in Image Recognition Feature Selection

Jin Zhang

Published Dec 29, 2025• 0 citations
Publisher

Abstract

In image recognition, the high-dimensional image feature leads to the phenomenon of "curse of dimensionality", which greatly limits the training speed and generalization performance of classification model. Therefore, this paper proposes to introduce parallelism and superposition property of quantum computing into feature selection. First, based on the concept of superposition states of quantum bits, the probability distribution of feature subsets is constructed. Second, the quantum rotating gate model is used to simulate the coherent evolution process of quantum states. Finally, the quantum tunneling effect is introduced to design the mutation strategy. Experimental results show that, compared with the traditional feature selection algorithm, the proposed method can achieve 95.2% accuracy on the test set, while only selecting 18.5% of features, and the accuracy will only drop 7.2% in high-dimensional situation. The experimental results also verify that, through the parallel search and quantum tunneling property, the quantum inspired mechanism provides an effective solution to overcome the performance limitation of traditional feature selection algorithm.

DOI
10.1109/cnc68716.2025.11484815

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In image recognition, the high-dimensional image feature leads to the phenomenon of "curse of dimensionality", which greatly limits the training speed and generalization performance of classification model. Therefore, this paper proposes to introduce parallelism and superposition property of quantum computing into feature selection. First, based on the concept of superposition states of quantum bits, the probability distribution of feature subsets is constructed. Second, the quantum rotating gate model is used to simulate the coherent evolution process of quantum states. Finally, the quantum tunneling effect is introduced to design the mutation strategy. Experimental results show that, compared with the traditional feature selection algorithm, the proposed method can achieve 95.2% accuracy on the test set, while only selecting 18.5% of features, and the accuracy will only drop 7.2% in high-dimensional situation. The experimental results also verify that, through the parallel search and quantum tunneling property, the quantum inspired mechanism provides an effective solution to overcome the performance limitation of traditional feature selection algorithm.
Feature selection is a critical step in many machine learning pipelines, aiming to reduce dimensionality and improve model performance by identifying the most relevant features from a large set of potential predictors. Traditional feature selection methods often struggle with the computational complexity that arises in high-dimensional data. This paper explores the application of quantum-inspired algorithms to feature selection, drawing inspiration from concepts like superposition and interference. We propose a novel approach that represents feature subsets as quantum states and utilizes interference patterns to prioritize features exhibiting stronger correlations with the target variable. Our method leverages probabilistic modeling and Monte Carlo simulations to approximate the quantum interference process, offering a potentially efficient alternative to exhaustive search and some traditional feature selection techniques. The core claim of this work is that quantum concepts can be effectively employed to accelerate feature selection in high-dimensional data. We demonstrate the feasibility of this approach through a theoretical framework and outline the key elements required for its practical implementation.
Image classification is an important task in the field of machine learning and image processing. However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming. Especially in the era of big data, the problem is prominent when the amount of images to be classified is large. In this paper, we try to use the powerful parallel computing ability of quantum computers to optimize the efficiency of image classification. The scheme is based on quantum K Nearest-Neighbor algorithm. Firstly, the feature vectors of images are extracted on classical computers. Then the feature vectors are inputted into a quantum superposition state, which is used to achieve parallel computing of similarity. Next, the quantum minimum search algorithm is used to speed up searching process for similarity. Finally, the image is classified by quantum measurement. The complexity of the quantum algorithm is only O((kM)^(1/2)), which is superior to the classical algorithms. Moreover, the measurement step is executed only once to ensure the validity of the scheme. The experimental results show that, the classification accuracy is 83.1% on Graz-01 dataset and 78% on Caltech-101 dataset, which is close to existing classical algorithms. Hence, our quantum scheme has a good classification performance while greatly improving the efficiency.
This paper introduces a novel feature selection algorithm based on quantum machine learning, designed to optimize the selection of relevant features for machine learning models. Traditional feature selection methods often rely on classical computational techniques, which can be limited in their ability to effectively handle complex feature spaces and intricate relationships. This research explores the potential of quantum mechanics to enhance feature selection by leveraging quantum properties, specifically superposition and entanglement, to efficiently explore and prioritize the most impactful features. We present a theoretical framework and initial experimental results demonstrating the algorithm's superior performance in selecting relevant features compared to existing classical methods. The core mechanism centers around quantum-enhanced feature representation and a novel quantum optimization process for feature ranking. This work holds promise for improving the accuracy and efficiency of machine learning models, particularly in high-dimensional datasets.
High-dimensional feature selection remains a critical challenge in machine learning, particularly when dealing with datasets containing a large number of potential predictive variables. Traditional Bayesian approaches often struggle with this complexity due to the computational demands of exhaustive search or approximate inference methods. This paper proposes a novel approach leveraging quantum-inspired techniques to accelerate the feature selection process. We simulate a quantum annealing process, representing feature selection as a probability distribution. Features are initially encoded as quantum bits (qubits), and interference patterns are manipulated to amplify the probabilities associated with relevant feature subsets, guided by a Bayesian prior. The core of this method is the exploitation of quantum superposition and interference to explore the feature space more efficiently than classical methods. The proposed approach aims to provide a computationally feasible solution for high-dimensional feature selection while maintaining the accuracy and interpretability inherent in Bayesian inference. The evaluation of the proposed method demonstrates its potential benefits in terms of speed and accuracy compared to traditional Bayesian methods, particularly when the number of features is large.
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Quantum Machine Learning (QML) offers a paradigm shift in computational power and algorithmic capabilities, potentially revolutionizing various fields, including machine learning. This research explores the application of quantum algorithms to enhance feature selection, a crucial step in many machine learning models. We introduce a novel quantum-enhanced feature selection algorithm, termed "Quantum Feature Selection," which leverages quantum entanglement and superposition to dynamically adjust feature importance during the training process. The core mechanism centers around exploiting these quantum phenomena to guide the selection of the most relevant features, ultimately leading to improved model accuracy and robustness. The algorithm's design aims to surpass existing static feature selection techniques by offering a dynamically adaptive approach. This work presents a comprehensive analysis of the algorithm's performance, including theoretical justifications and experimental validation utilizing a diverse dataset. This research is motivated by the potential to address challenges in traditional machine learning, specifically concerning the effective handling of high-dimensional data and the optimization of complex models. Quantum Machine Learning has the potential to provide solutions that are far beyond traditional computational methods.