The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing $A I$ techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms.
The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing AI techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms.
This rapid growth in data and the increased complexity of machine learning models have made it almost impossible to continue pushing forward with classical computing, thereby creating a pressing need for new computational paradigms. The following paper will examine how quantum machine learning answers the above questions by applying the principles of quantum mechanics to the art of artificial intelligence (AI) and big data analytics. We examine some of the current Quantum Machine Learning (QML) algorithms and their applications based on a framework that employs quantum-enhanced feature spaces, variational quantum circuits, and quantum-inspired classical algorithms. Our methodology combines theoretical analysis with empirical evaluation of these methods over standard benchmark datasets, using both quantum simulators and real quantum hardware. Key findings include a set of advantages carried by QML in specific fields, such as dimensionality reduction, optimization problems, and some clustering tasks. These are the quadratic speedup for support vector machines and the exponential advantage for principal component analysis. Our results also show that quantum error correction isn’t perfect and that loading classical data into a quantum system can be hard. We conclude that, while QML holds much promise, it is, by all means, clearly still a way off from being considered a panacea. It is awaiting further breakthroughs in quantum hardware, error mitigation techniques, and algorithm design in order to fully realize its potential. This work thus presents a roadmap for future research and highlights where QML is best placed to make major impactful differences in artificial intelligence and big data analytics.
This paper investigates the potential of utilizing multi-qubit entanglement for enhancing the performance of quantum machine learning (QML) algorithms. Current QML approaches often face limitations in data representation and feature extraction, particularly when dealing with high-dimensional datasets. We propose a novel framework that leverages the inherent properties of multi-qubit entanglement to address these challenges. The core idea involves designing an algorithm that exploits entanglement for data compression and feature extraction, leading to improved computational efficiency and potentially increased accuracy. Specifically, we explore the use of entangled states to create richer data representations and perform more efficient pattern recognition. The theoretical framework is presented, outlining the key components and demonstrating the potential advantages of this approach. While a full experimental implementation is not presented due to the current limitations of hardware, we provide a detailed analysis and a roadmap for future research in this area. The central claim of this work is that utilizing multi-qubit entanglement can significantly improve the performance of QML algorithms.
This research focuses on the implementation of quantum machine learning with the classical models such as Gradient Boosting and K-means clustering for efficient classification and clustering of most complex datasets in advanced systems. Taking advantage of the large datasets that are handled by the qc, this work examines Quantum Support Vector Machines (QSVM), Quantum Boosting, and Quantum K-means. To compare the efficiency of these hybrid models three datasets - the healthcare, the financial, and the cyber-security datasets - are used. Our findings indicate that quantum optimization algorithms achieve better accuracy in clustering in comparison to the classical counterparts and are computationally faster. This work shows how quantum machine learning can lead to the enhancement of difficult data processing tasks, a major issue in the quantum computing domain, with specific reference to resources and high-dimensional data. The opportunity to integrate quantum and classical models successfully is the best basis for further developments in machine learning.
Quantum-enhanced machine learning (QML) is transforming artificial intelligence through the application of quantum computing concepts to solving computationally challenging problems more effectively than conventional methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the capability to speed up deep learning model training, solve combinatorial optimization problems, and improve feature selection in high-dimensional space. It covers basic quantum computer concepts employed within AI, for example, quantum circuits, quantum variational algorithms, and kernel quantum methods, and their impacts on neural networks, generative models, and reinforcement learning. It further refers to the hybrid quantum-classical architectures in AI where a combination of quantum subroutines and classical deep learning models are employed together with the purpose to gain computational speedup in optimization and handling massive data. Despite the transformative promise of quantum AI, technical issues of qubit noise, error correction, and scaling hardware continue to hold back full implementation. This contribution offers a qualitative overview of quantum-enhanced AI, surveying current applications, research endeavors, and upcoming innovation in quantum deep learning, autonomous systems, and scientific computing. The results open the door for large-scale quantum machine learning architectures, which provide new solutions to future uses of AI in finance, medicine, cyber security, and robotics.
The rapid advancement of quantum computing hardware and software has ushered in a new era for computational science, promising to tackle problems that are intractable for classical computers. Parallelly, machine learning (ML) has revolutionized data analysis and artificial intelligence. The confluence of these two fields, known as Quantum Machine Learning (QML), aims to harness the principles of quantum mechanics-such as superposition, entanglement, and interference-to enhance and redefine classical machine learning algorithms. This survey provides a comprehensive overview of the burgeoning field of QML. We begin by elucidating the fundamental quantum concepts that underpin QML approaches. We then present a detailed taxonomy and analysis of prominent QML algorithms, categorizing them into quantum-enhanced classical models and fully quantum models. Key families of algorithms discussed include Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNNs), and Quantum Generative Adversarial Networks (QGANs). For each, we explain the theoretical quantum advantage, the circuit implementation, and the current limitations. Furthermore, we delve into the critical challenges facing the practical realization of QML, including the pervasive issue of noise in Noisy Intermediate-Scale Quantum (NISQ) devices, the problem of barren plateaus in training quantum circuits, and the complexities of efficient data encoding (quantum feature maps). We also present a small-scale experimental simulation of a variational quantum classifier on a standardized dataset, demonstrating its principle and comparing its performance with a classical counterpart. Finally, the paper discusses the future trajectory of QML, highlighting the importance of error mitigation, hybrid quantum-classical architectures, and the development of quantum-specific learning inductive biases. We conclude that while fault-tolerant quantum computers remain on the horizon, QML represents a paradigm shift with the potential to deliver profound computational advantages in the coming decades.
Clustering is one of the most fundamental tasks in machine learning, and the k-means clustering algorithm is perhaps one of the most widely used clustering algorithms. However, it suffers from several limitations, such as sensitivity to centroid initialization, difficulty capturing non-linear structure, and poor performance in high-dimensional spaces. Recent work has proposed improved initialization strategies and quantum-assisted distance computation, but the similarity metric itself has largely remained classical. In this study, we propose a quantum-enhanced variant of k-means that replaces the Euclidean distance with a quantum kernel derived from the inner product between feature-mapped quantum states. Using the Iris dataset, we use multiple quantum feature maps, including entangled SU2 and ZZ circuits, to embed classical data into a higher-dimensional Hilbert space where cluster structures become more separable. We will also be testing using another dataset, namely the breast cancer dataset. Similarity between data points is computed through the inner product between two states. Our results show that this approach achieves improved clustering stability and competitive accuracy compared to the classical algorithm, with the SU2 feature map yielding an accuracy of 88.6 % on the Iris dataset and 91.0 % on the breast cancer dataset, despite operating on NISQ-feasible shallow circuits. These findings suggest that quantum kernels provide a richer similarity landscape than traditional distance metrics, offering a promising path toward more robust unsupervised learning in the NISQ era.