In this paper, a hybrid model framework based on quantum computing optimization is proposed for resource demand forecasting and data classification tasks. The study first constructs an AR model for demand forecasting, determines the lag order by PACF and ACF, and transforms the model parameters into QUBO form for optimization and solution after discretization. Secondly, for SVM classification, the penalty function method is adopted to transform the constraints into unconstrained optimization, and the QUBO model is constructed to achieve classification boundary optimization by discretizing the decision variables and introducing penalty function terms. Finally, in the field of deep learning, a parameter optimization method based on Adam's algorithm is proposed to construct a CNN model by combining the ReLU activation function, and the loss function is transformed into a QUBO matrix by parameter quantization technique, and the optimal parameter configurations are solved by using quantum computation. The results show that the framework achieves good results in both prediction accuracy and classification performance, providing a new solution for complex optimization situations.
In this paper, a hybrid model framework based on quantum computing optimization is proposed for resource demand forecasting and data classification tasks. The study first constructs an AR model for demand forecasting, determines the lag order by PACF and ACF, and transforms the model parameters into QUBO form for optimization and solution after discretization. Secondly, for SVM classification, the penalty function method is adopted to transform the constraints into unconstrained optimization, and the QUBO model is constructed to achieve classification boundary optimization by discretizing the decision variables and introducing penalty function terms. Finally, in the field of deep learning, a parameter optimization method based on Adam's algorithm is proposed to construct a CNN model by combining the ReLU activation function, and the loss function is transformed into a QUBO matrix by parameter quantization technique, and the optimal parameter configurations are solved by using quantum computation. The results show that the framework achieves good results in both prediction accuracy and classification performance, providing a new solution for complex optimization situations.
This study focuses on the integration of quantum computing and artificial intelligence, proposing a multi-task optimisation framework based on the quantum annealing algorithm to address the bottlenecks in computational efficiency and model optimisation in traditional machine learning. The study converts problems such as time series prediction, Support Vector Machine (SVM) classification, and Convolutional Neural Network (CNN) training into Quadratic Unconstrained Binary Optimisation (QUBO) models and achieves parameter optimisation through the simulated annealing algorithm. In time series prediction, an autoregressive model is constructed based on historical data. The problem of minimising prediction error is mapped to a binary optimisation problem through QUBO transformation, and the model parameters are solved using the simulated annealing algorithm to achieve trend prediction of the target variable, validating the effectiveness of quantum optimisation in time series analysis. For classification tasks, the hinge loss and regularisation terms of SVM are converted into a QUBO matrix to optimise the classification hyperplane parameters, thereby enhancing the model's classification performance in the feature space. The results demonstrate that this method exhibits strong geometric interpretability and generalisation capabilities in binary classification problems. In the field of deep learning, L2 regularisation is introduced into the weight optimisation of CNNs, and a QUBO model is constructed to explore the application of quantum optimisation in image feature extraction and network training. Although limited by the scale of variables, this demonstrates the potential value of combining quantum computing with deep learning. The study shows that the quantum optimisation framework can effectively improve solution efficiency in small- to medium-scale problems, providing new technical pathways for time series analysis, classification tasks, and deep learning optimisation.
This paper proposes an integrated QUBO-based quantum-annealing optimization framework for next-generation cloud computing. First, for resource-demand forecasting, we blend Autoregressive (AR) models with Quadratic Unconstrained Binary Optimization (QUBO) and solve the resulting problem via Simulated Annealing; monthly-scale tests reveal superior trend-capture accuracy compared with classical predictors. Second, we embed quantum optimization into Support Vector Machines by casting kernel selection and penalty-parameter tuning as QUBO instances; evaluations on the Iris benchmark—and on synthetically enlarged variants—show marked gains in both classification accuracy and training speed, especially under large-data regimes. Third, we extend the same paradigm to deep learning: a QUBO-formulated quantum-annealing routine optimizes weight initialization and layer-wise learning rates in convolutional networks for image classification, yielding higher top-1 accuracy and reduced wall-clock training time. Across all tasks the hybrid quantum-classical solver consistently outperforms its classical counterparts while maintaining linear scalability in cloud environments. These results show the practical potential of quantum - enhanced algorithms for resource prediction, classification, and deep learning, and pave the way for wider use of quantum technologies in cloud - based AI services.
Classification is a fundamental task in machine learning that aims to categorize input samples into predefined classes based on their features. It has widespread applications in areas such as spam detection and image classification. Support Vector Machines (SVM) are a widely-used supervised learning method that seeks to maximize the margin between decision boundaries, particularly excelling in non-linear classification tasks through kernel functions. In this study, the SVM classification problem is reformulated as an optimization problem and represented using Quadratic Unconstrained Binary Optimization (QUBO) to leverage quantum computing for solving. To enhance the solution process, the simulated annealing algorithm is employed, improving convergence speed and boosting both the efficiency and performance of the classification task.
This paper deals with the research of QUBO model optimization machine learning algorithm, including the following: first, discretize the continuous parameters of the machine learning model into binary variables, and construct the QUBO objective function; then optimize the QUBO model by using the quantum annealing algorithm; and finally solve the globally optimal solution by adjusting the annealing parameters, which include the initial temperature, the coefficient of temperature reduction, and the number of iterations. QUBO model can discretize continuous variables (including parameters such as weights and bias) in AR, SVM, CNN and other models in machine learning into binary variables, so as to better deal with nonlinear relationships and reduce the computational complexity in the training process. With the QUBO model, the regularization term can be better controlled to avoid the overfitting problem. Meanwhile, the QUBO model can be solved in parallel by quantum computing or simulated annealing algorithm, which significantly improves the computational efficiency under large-scale data.
Diagnosing multimodal medical conditions necessitates seamlessly integrating the insights of both medical images and physiological signals, aiming to boost disease detection accuracy and efficiency in clinical decision making. However, existing deep learning models struggle to exploit sophisticated cross-modal relations and higher-order feature interactions efficiently and computationally. To overcome such shortcomings, we propose a Quantum-Deep Hybrid Architecture to jointly learn the healthcare signal and image features by incorporating Variational Quantum Circuits (VQCs) into a combined architecture based on Convolutional Neural Networks (CNNs) and 1D-CNNs. In our approach, medical image data and physiological signal data pass through distinct CNN and 1D-CNN branches to extract spatial and temporal features, respectively. These encoded, lower-dimensional feature vectors are encoded into quantum states via angle encoding and learn quantum-enhanced features via a Variational Quantum Circuit. The derived quantum and classical features are fused via fully connected layers and a softmax activation function for classification. Moreover, we exploit the Bayesian optimization to select the optimum hyper-parameters and integrate both privacy-preserving feature encoding and secure data processing into the framework. Empirical evaluation results reveal that the developed model achieves overall classification accuracy, precision, recall, F1-score of 94.8%, 95.1%, 94.3%, 94.7%, respectively, with an average AUC of 0.968, surpassing the contemporary CNN and multimodal deep learning baselines. Our proposed hybrid model also shows competitive performance under varying levels of noise and demonstrates excellent stability. The results also suggest the developed framework offers an efficient, robust and scalable approach to next-generation multimodal healthcare analytics and intelligent clinical decision-making systems.
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.