Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. This work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. The quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. These results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems.
Integrating mobile intelligence with quantum-inspired machine learning (QiML) opens space for challenging mobile signal processing tasks. By utilizing superposition and entanglement, quantum computing principles, QiML boosts the agility of mobile devices by allowing real-time data processing, pattern recognition, and decision making. This work introduces Quantum-Inspired Mobile Intelligence (QiMINT) a new mobile computing framework that integrates quantum-inspired designs with classical machine learning, which increases mobile devices' accuracy, latency, and energy efficiency. Results show that medical QiML based models exceeded traditional machine learning methods in most key performance indicators. The quantum convolutional neural network (QCNN) achieved an accuracy of 92.3% in contrast with the CNNs' 87.5%, but with a processing time of 80 ms as to 120 ms, energy consumption of 10 mJ in comparison to the CNNs' 15mJ. Also, quantum-inspired random forests lowered processing delay by 40%, sustaining superior accuracy than the classical-base system. These results demonstrate that QiML can effectively balance computation complexity while making it suitable for edge computing, IoT, and mobile intelligence systems.
This study aims to evaluate the effectiveness of quantum-inspired machine learning (QIML) techniques when applied to high-dimensional data processing problems. Instead of relying on quantum hardware, QIML uses concepts such as amplitude encoding and tensor networks to improve the efficiency of traditional models. We compared the performance of QIML models with traditional machine learning models on three real-world datasets in the fields of finance, medical diagnosis, and natural language processing. The study used metrics of accuracy, training speed, and generalization properties for comparison. Our results show that hybrid QIML models achieve up to a 10% improvement in accuracy and a 40% improvement in training speed compared to traditional models. We also discuss current limitations in scalability and computational cost and suggest future research directions for developing QIML as an effective tool for processing complex data.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The exponential growth of complex datasets has exposed the limitations of classical machine learning algorithms in speed, scalability, and pattern detection capabilities. Quantum Machine Learning (QML) integrates quantum computation principles—including superposition, entanglement, and quantum interference—into machine learning pipelines to accelerate data processing and optimize pattern recognition. This paper analyzes the theoretical foundations, algorithmic frameworks, performance advantages, hardware feasibility, and real-world applications of QML models, particularly in high-complexity systems such as genomics, financial analytics, cybersecurity, and climate modeling. Through analytical comparisons, datasets, and quantum simulator performance benchmarks, results demonstrate that QML models outperform classical ML by factors ranging from 10× to 1000× in processing speed and accuracy for high-dimensional nonlinear datasets. Critical challenges including qubit instability, data encoding overheads, and hardware scalability are discussed along with emerging solutions like variational quantum circuits and hybrid quantum-classical architectures. The findings establish QML as a foundational technology for future artificial intelligence advancements in ultra-large-scale data environments.
The blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.
Quantum machine learning (QML) is transforming the field of machine learning (ML), which typically utilizes a quantum computing such as gate model-based and annealing-based computing. Recently, Ising solver chips have been developed to emulate the superposition and entanglement properties of quantum physics using the current silicon manufacturing technologies. The chips have potential to solve complex combinatorial optimization problems (COP) with significantly low power consumption and price. This paper proposes quantum-inspired ML (QiML) framework that provides an approach to train the traditional ML models using a physics-based Ising solver chip to develop QiMLs. To validate the feasibility of the proposed framework, a quantum-inspired support vector machine (QiSVM) is developed and the results are compared with a quantum SVM trained by a commercial D-Wave quantum annealer and a conventional SVM to solve real-world problems. The proposed QiML framework will open new frontiers for advancing ML technologies for industry cyber-physical system applications.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics