Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

neural information processing systems

Learning to Solve Quadratic Unconstrained Binary Optimization in a Classification Way

Ming Chen, Yingwu Chen, Shang Xiang, Luona Wei, Yonghao Du, Qian Wan, Yuning Chen, Yingwu Chen

Published Jan 1, 2024• 1 citation
Publisher
DOI
10.52202/079017-3636

Related papers

Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization

Ming Chen, Yuning Chen, Yonghao Du +2 more

Knowledge-Based Systems•Aug 8, 2020•18 citations

Learning Quadratic Unconstrained Binary Optimization Models for Quantum Annealing

Florian Richoux, Jean-François Baffier, Philippe Codognet

•Jan 1, 2025•0 citations

Support vector machine based on the quadratic unconstrained binary optimization model

Qiwei Wang

Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.

Journal of Physics Conference Series•Oct 1, 2024•1 citation

The Quadratic Unconstrained Binary Optimization Problem

Springer eBooks•Jan 1, 2022•65 citations

Research on accelerated solution of SVM classification task based on quantum computing

teng zhang, Kai Li, Yao Fu +1 more

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.

International Conference on Electronic Information Engineering and Data Processing (EIEDP)•May 9, 2025•1 citation

Binary Unconstrained Quadratic Optimization Problem

Gary A. Kochenberger, Fred Glover, Haibo Wang

Springer eBooks•Jan 1, 2013•7 citations
Abstract Support vector machine (SVM) is a powerful supervised machine learning model that is often used in binary classification algorithms. As Moore’s Law approaches its theoretical limits and the demand for machine learning to handle large-scale, high-dimensional data analysis intensifies, the necessity of adopting non-traditional computational approaches becomes evident. Quantum computing, in particular, emerges as a vital solution for the effective training of SVM models, providing capabilities beyond those of classical computing systems. To solve the above problems, a QUBO (quadratic unconstrained binary optimization) model is proposed to transform the SVM machine learning model into a quadratic unconstrained binary optimization problem so that they can be effectively trained on the D-Wave platform using adiabatic quantum computer. The results show that the QUBO model can transform the SVM model into a simple quadratic programming problem, which makes it suitable for adiabatic quantum computer processing. When processing large-scale and high-dimensional data, this transformation shows a natural advantage and significantly improves computational efficiency. The application potential of this transformation technology is huge in the medical field.
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.