Abstract The K‐nearest neighbor algorithm is one of the most frequently applied supervised machine learning algorithms. Similarity computing is considered to be the most crucial and time‐consuming step among the classical K‐nearest neighbor algorithm. A quantum K‐nearest neighbor algorithm is proposed based on the divide‐and‐conquer strategy. A quantum circuit is designed to calculate the fidelity between the test sample and each feature vector of the training dataset. The quantum K‐nearest neighbor algorithm has higher classification efficiency in high‐dimensional data processing. The classification accuracy of the proposed algorithm is equivalent to that of the classical K‐nearest neighbor algorithm under the IRIS dataset. In addition, compared with the typical quantum K‐nearest neighbor algorithms, the proposed classification method possesses higher classification accuracy with less calculation time, which has wide applications in the industrial field.
Abstract The K‐nearest neighbor algorithm is one of the most frequently applied supervised machine learning algorithms. Similarity computing is considered to be the most crucial and time‐consuming step among the classical K‐nearest neighbor algorithm. A quantum K‐nearest neighbor algorithm is proposed based on the divide‐and‐conquer strategy. A quantum circuit is designed to calculate the fidelity between the test sample and each feature vector of the training dataset. The quantum K‐nearest neighbor algorithm has higher classification efficiency in high‐dimensional data processing. The classification accuracy of the proposed algorithm is equivalent to that of the classical K‐nearest neighbor algorithm under the IRIS dataset. In addition, compared with the typical quantum K‐nearest neighbor algorithms, the proposed classification method possesses higher classification accuracy with less calculation time, which has wide applications in the industrial field.
ABSTRACT A quantum K‐nearest neighbors(QKNN) algorithm is proposed to offer superior performance compared to the classical KNN(CKNN) approach, improving classification accuracy, scalability, and robustness. Our approach optimizes Hadamard and rotation gates for quantum data encoding and efficiently embeds classical data into quantum states. Entangled gates, such as IsingXY and CNOT, enhance feature extraction and classification by enabling complex feature interactions. A new quantum distance metric based on swap test results is used to calculate similarity measures between quantum states. This algorithm offers superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We used three benchmark datasets to evaluate the suggested QKNN method. The results demonstrated that it outperformed the other two methods, classical KNN (CKNN) and quantum neural networks (QNN), as well as the more recent QKNN research. The proposed QKNN algorithm achieves prediction accuracies of 98.25%, 100%, and 99.27% for the three datasets, whereas the QNN achieves prediction accuracies of 97.17%, 83.33%, and 86.18%, respectively. Moreover, quantum noise challenges are addressed by integrating a Shor code‐based error mitigation strategy, which ensures stability of the algorithm and resilience to noisy quantum environments. The results demonstrate the scalability, efficiency, and robustness of the proposed QKNN algorithm.
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.
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.
Text classification is one of key problems in pattern recognition. The KNN algorithm is a widely used text classification algorithm, because it is simple, valid and non-parameters. The main idea of KNN algorithm is to calculate the similarity between the new sample with unknown class label and the training samples, and choosing the class label of the highest k nearest neighbors as the new sample's class label. However, the text contains hundreds and thousands of features. The similarity computing in large numbers of vector will cost many time. In fact that, many machine learning algorithms are unable to manipulate and compute large number of vectors in high-dimensional space. Quantum computing algorithms are good at computing high-dimensional vectors in large tensor product spaces. It can provide exponential speed-up over its classical counterparts. The N-dimensional quantum vectors represent 2N quantum superposition states. The similarity computing on the N-dimensional vectors is also on the 2N quantum superposition states simultaneously. Therefore, this paper introduces a KNN algorithms based on quantum computing, which uses fidelity to compute the similarity between two quantum states. The Control-Swap Test gate is very convenient as a fidelity estimator.
This paper presents a novel quantum K-nearest neighbors (QKNN) algorithm, which offers improved performance over the classical k-NN technique by incorporating quantum computing (QC) techniques to enhance classification accuracy, scalability, and robustness. The proposed modifications focus on optimizing quantum data encoding using Hadamard and rotation gates, ensuring more effective rendering of classical data in quantum states. In addition, the quantum feature extraction process is significantly enhanced by the use of entangled gates such as IsingXY and CNOT, which enables better feature interactions and class separability. A novel quantum distance metric, based on the swap test, is introduced to calculate similarity measures between various quantum states, offering superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We assess the achievement of the proposed QKNN algorithm on three benchmark datasets: Wisconsin Breast Cancer, Iris, and Bank Note Authentication, and have noted its superior performance relative to both classical k-NN (CKNN) and Quantum Neural Network (QNN). The proposed QKNN algorithm is found to achieve prediction accuracies of 98.25%, 100%, and 99.27% ,respectively, for the three datasets, while the customized QNN shows prediction accuracies of only 97.17%, 83.33%, and 86.18%, respectively. Furthermore, we address the challenges of quantum noise by incorporating a repetition encoding-based error mitigation strategy, which ensures the stability and resilience of the algorithm in noisy quantum environments. The results highlight the potential of the proposed QKNN as a scalable, efficient and robust quantum-enhanced machine learning algorithm, especially in high-dimensional and complex datasets, when traditional approaches frequently fail.
Dealing with high-dimensional data is a well-known limitation for the classic K-Nearest Neighbors (KNN) algorithm, creating substantial computational and memory overhead. While Quantum Machine Learning (QML) presents a compelling alternative, many existing Quantum KNN (QKNN) frameworks are largely theoretical, remaining impractical for current hardware due to their significant qubit demands. This paper introduces the Fidelity-Based Quantum K-Nearest Neighbors (FQKNN) algorithm, a method specifically designed for feasibility on near-term devices. Our primary contribution is the use of quantum fidelity as a similarity function, which offers a more nuanced metric than conventional distance calculations. This is combined with an efficient amplitude encoding strategy that logarithmically scales the qubit requirement to just log2(N) for an N-dimensional feature vector. This combination makes FQKNN a resource-efficient and practical algorithm. We validated its performance on the benchmark Iris and Breast Cancer datasets, achieving classification accuracies of 96.7% and 93.3% respectively, surpassing the performance of the classical KNN baseline. These findings not only confirm the viability of our fidelity-based similarity measure but also underscore the demonstrable potential of QML to enhance classification tasks.