Advanced Quantum Computing–Integrated Artificial Intelligence for Data Processing Applications: A Comprehensive Review

Review of Quantum Machine Learning Integration in Modern Computing

Quantum Machine Learning (QML) is a new multidisciplinary field that exploits the computational power of quantum computing for machine learning problems. Current research provides an overview of important QML algorithms, such as Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), Quantum Approximate Optimization Algorithm (QAOA), and hybrid quantumclassical computing techniques, which have recently become more popular owing to hardware constraints in the NISQ period. Analysis reveals that QML provides an edge in working with high-dimensional data, optimization, and probabilistic modeling. Applications of the technology include various areas such as diagnostics, drug discovery, financial models, cybersecurity, and quantum cryptography (Quantum Key Distribution, QKD). Nevertheless, QML technology suffers from multiple difficulties, including the lack of scalable qubits, noise and decoherence, inefficient encoding methods, absence of standard benchmarking, and interpretability of models. Moreover, hybrid computing architecture and nascent quantum software development are also barriers.

Quantum-Enhanced Artificial Intelligence: Bridging Quantum Physics and Machine Learning for Next-Generation Computing Paradigms

A conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning is presented.

Quantum Artificial Intelligence opportunities, challenges, threats, use case and future trends

The field of quantum computing is presently undergoing a significant transition. Even if quantum computers are now so large and capable that they can only be used under certain limitations, be conventionally emulated, it is still unknown which applications can benefit from a quantum advantage. With an emphasis on artificial intelligence (AI) and its applications as some of the most significant key technologies of our time, this provided an excellent starting point for looking beyond the already demonstrated acceleration of a few specific classical computing tasks by quantum computers, such as unstructured search and prime factorization. Actually, the goal of Quantum Artificial Intelligence (QAI) is to merge the two worlds for each’s benefit. Stated differently, QAI research focuses on using quantum computing to solve computationally challenging AI problems and, conversely, using AI to address the difficulties associated with developing and running quantum computing systems. The creation of direct quantum or hybrid quantum-classical algorithms for problem solving and the examination of their possible benefits over the best classical alternatives are of particular interest.

Quantum-Enhanced ANN for Attack Detection in Software-Defined Networking: A Hybrid Learning Model

Quantum Computing-Driven Optimization of Machine Learning Algorithms for Embedded Systems

The development of quantum technologies opens new possibilities for the optimization of machine learning (ML) algorithms. In this contribution, we report on the experimental use of quantum computing for training ML algorithms and models designed to be deployed for inference on embedded systems with limited computational resources. Due to the small size of resource-efficient models for embedded systems, this application of quantum computing provides an excellent opportunity to evaluate a technology that is currently being developed in the noisy intermediate-scale quantum era. We develop an approach based on transforming classical supervised learning problems into a discrete form, enabling their solution using quantum annealing (QA) methods. Three models are presented: a binary XNORNet network, a support vector machine (SVM) with a discrete representation of dual coefficients, and a convolutional neural network. All experiments are performed on the MNIST dataset, using classical optimization methods, simulated annealing, and QA on the D-Wave Advantage system. While classical gradientbased methods maintain superior absolute accuracy (up to 95.97% for SVM), the D-Wave system demonstrates a significant reduction in core optimization time, achieving speedups of up to 15.6× compared to classical optimization on a central processing unit when the system overhead is excluded. Preliminary results suggest that while QA entails a slight accuracy and memory requirements penalty, a drastic reduction in active annealing duration serves as a promising strategy to reduce training latency in future embedded-focused ML applications.

Quantum Machine Learning: Foundational Principles to Practical Applications

This survey provides a comprehensive overview of the theoretical foundations, unified taxonomy, and key methodologies in QML, and discusses quantum data encoding techniques, variational quantum models, neural-inspired quantum architectures, and quantum data learning approaches, along with their associated challenges and limitations.