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International Conference on Natural Computation (ICNC)

Feature Selection based on QFT

Ri‐Gui Zhou, Nan Jiang, Shuqun Yang, Ding Qiu-lin

Published Jan 1, 2007• 1 citation
Publisher

Abstract

Employing the powerful tool of the Quantum Fourier Transform, This paper presents an algorithm of pattern feature selection.Moreover.the proposed algorithm can perform feature selection tasks exponentially faster than its classical counterpart due to quantum parallelism. The features extracted by this algorithm are helpful to the pattern recognition or image recognition.And this paper proved the feasibility of the algorithm through theory deduction and validate serviceability by a simple pattern figure.

DOI
10.1109/icnc.2007.384

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Employing the powerful tool of the Quantum Fourier Transform, This paper presents an algorithm of pattern feature selection.Moreover.the proposed algorithm can perform feature selection tasks exponentially faster than its classical counterpart due to quantum parallelism. The features extracted by this algorithm are helpful to the pattern recognition or image recognition.And this paper proved the feasibility of the algorithm through theory deduction and validate serviceability by a simple pattern figure.
This survey presents a comprehensive analysis of the quantum Fourier transform (QFT), a fundamental tool in quantum computing, in comparison to its classical counterpart, the fast Fourier transform (FFT). The study begins with an introduction to the classical Fourier transform, which is widely used in different disciplines such as signal and image processing. The article introduces the QFT as a quantum version of the Fourier transform, detailing how it leverages quantum parallelism and superposition to reduce the time complexity of the operation, and highlighting its crucial role in quantum algorithms like Shor’s algorithm for integer factorization. The analysis also addresses the mathematical foundations of the QFT, its implementation in quantum circuits, and the key advantages and challenges associated with its use, such as measurement precision and quantum decoherence. Finally, it concludes with an exploration of the current and potential applications of QFT in quantum computing.
Quantum image recognition is a technology by using quantum algorithm to process the image information. It can obtain better effect than classical algorithm. In this paper, four different quantum algorithms are used in the three stages of palmprint recognition. First, quantum adaptive median filtering algorithm is presented in palmprint filtering processing. Quantum filtering algorithm can get a better filtering result than classical algorithm through the comparison. Next, quantum Fourier transform (QFT) is used to extract pattern features by only one operation due to quantum parallelism. The proposed algorithm exhibits an exponential speed-up compared with discrete Fourier transform in the feature extraction. Finally, quantum set operations and Grover algorithm are used in palmprint matching. According to the experimental results, quantum algorithm only needs to apply square of N operations to find out the target palmprint, but the traditional method needs N times of calculation. At the same time, the matching accuracy of quantum algorithm is almost 100%.
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.
Dimensionality reduction is the fundamental problem for machine learning and pattern recognition. During data preprocessing, the feature selection is often demanded to reduce the computational complexity. The problem of feature selection is categorized as a NP optimization problem. Exhaustive search of huge set of features takes huge amount of time on classical computer. In the present paper we discuss the role of quantum adiabatic computation to perform feature selection with bi-quadratic optimization and provide a quantum feature selection algorithm. Our algorithm runs with the quantum adiabatic time complexity bound O(1/gmin2)O(1/g_{min}^{2})O(1/gmin2​), which is better than classical approach for bi-quadratic feature selection.
Quantum Machine Learning Pattern Recognition explores the potential of quantum algorithms to revolutionize pattern recognition, particularly in the context of high-dimensional data. This paper introduces a novel approach utilizing quantum machine learning principles to enhance model accuracy and robustness, addressing limitations inherent in traditional machine learning methods. We detail the core mechanism—a hybrid quantum-classical approach—that leverages quantum feature extraction and parallel processing to overcome challenges associated with high-dimensional data, offering a paradigm shift in pattern identification. The core claim is that this method surpasses traditional approaches in handling complex, high-dimensional datasets, exhibiting enhanced generalization capabilities and a deeper understanding of underlying patterns. We provide a conceptual framework and preliminary results demonstrating the effectiveness of this approach, highlighting its potential impact across various domains.