Quantum machine learning (QML) and quantum communication (QC) are emerging areas of research for complexity-efficient data processing, information retrieval, system, and simulations. Towards next-generation vehicular technology, quantum circuits and algorithms are critical in providing faster and more reliable solutions. Here, we show a modified quantum K-nearest neighbour (QKNN) algorithm for symbol detection in the communication system. In the proposed algorithm, we embed a quantum swap-test circuit for the distance computation between unknown symbols with known training symbols with a memory-efficient strategy, which eliminated quantum search operations for the small-search space such as 16-QAM symbols. The proposed quantum circuit is simulated on an IBM quantum machine and the numerical results are shown to demonstrate the efficacy of the proposed framework. The classification accuracy as a performance metric is shown and the improvement of quantum resources in terms of gate complexity is discussed.
Quantum machine learning (QML) and quantum communication (QC) are emerging areas of research for complexity-efficient data processing, information retrieval, system, and simulations. Towards next-generation vehicular technology, quantum circuits and algorithms are critical in providing faster and more reliable solutions. Here, we show a modified quantum K-nearest neighbour (QKNN) algorithm for symbol detection in the communication system. In the proposed algorithm, we embed a quantum swap-test circuit for the distance computation between unknown symbols with known training symbols with a memory-efficient strategy, which eliminated quantum search operations for the small-search space such as 16-QAM symbols. The proposed quantum circuit is simulated on an IBM quantum machine and the numerical results are shown to demonstrate the efficacy of the proposed framework. The classification accuracy as a performance metric is shown and the improvement of quantum resources in terms of gate complexity is discussed.
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.
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.
Quantum computing has risen as the new type of computer that theoretically reduces the time complexity of classical methods exponentially because of the nature of superposition. It is promising to reduce runtimes on large data sets in machine learning (ML). Meanwhile, k-nearest neighbor (kNN) is a simple ML algorithm that can be translated to a quantum version (QkNN) to perform classification tasks efficiently. Here, we show a new version of QkNN which has a speed up in time complexity by using a new design of quantum circuits called integrated swap test. This quantum circuit can load two N-dimensional states and calculate the fidelity between them. Results show that QkNN allows us to take a different approach to the ML field.
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.
Nowadays, using machine learning for image classification is very common. However, due to the increasing demand for data processing and fast computing, the idea of enhancing machine learning with quantum computing has been proposed, known as quantum machine learning (QML). Quantum machine learning has the advantages of higher efficiency and accuracy. Quantum computing uses quantum bits (qubits) for data storage and computing, where a qubit can represent quantum states |0〉and |1〉simultaneously, enabling the processing of information for two states simultaneously, which is unparalleled in classical computing. Moreover, quantum machine learning can handle more complex data and process data faster. In classical machine learning, the processing of large-scale data and complex problems often faces problems of high computational complexity and low algorithm efficiency. Quantum computing can handle multiple computing tasks simultaneously, achieving faster computing. Therefore, in some scenarios that require efficient computing, quantum machine learning may be the best choice. In this study, we simulated quantum circuits using Qiskit and built a hybrid quantum-classical neural network model using VQNet to classify MNIST handwritten digits and CIFAR-10 datasets. The experiments showed that quantum machine learning has the advantages of efficiency, accuracy, and security over classical machine learning, which may be an improvement over classical machine learning. This research proposes a machine learning algorithm based on quantum computing, which promotes the development of quantum computing and quantum technology. At the same time, it provides a new solution and idea for image classification, enabling people to pursue faster and more accurate quantum machine learning instead of being limited to classical machine learning.[1]