To tackle environmental and social challenges, we face numerous tasks that need to be solved. However, due to limited time and resources, it is unrealistic to solve all tasks. Therefore, prioritizing tasks to ensure efficient handling and meeting criteria is essential. When the number of tasks is large, prioritizing the optimal combination of tasks on classical computers becomes highly time-consuming. We define this pri-oritization as a combinatorial optimization problem, termed the task-select problem, and address it using a quantum annealer. A quantum annealer is specialized hardware designed to efficiently solve combinatorial optimization problems by transforming them into Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we propose a QUBO formulation for the task-select problem. Experimental evaluations demonstrate that our approach enables solving the task-select problem with up to 100 items using a real quantum annealer.
To tackle environmental and social challenges, we face numerous tasks that need to be solved. However, due to limited time and resources, it is unrealistic to solve all tasks. Therefore, prioritizing tasks to ensure efficient handling and meeting criteria is essential. When the number of tasks is large, prioritizing the optimal combination of tasks on classical computers becomes highly time-consuming. We define this pri-oritization as a combinatorial optimization problem, termed the task-select problem, and address it using a quantum annealer. A quantum annealer is specialized hardware designed to efficiently solve combinatorial optimization problems by transforming them into Quadratic Unconstrained Binary Optimization (QUBO) problems. In this paper, we propose a QUBO formulation for the task-select problem. Experimental evaluations demonstrate that our approach enables solving the task-select problem with up to 100 items using a real quantum annealer.
This paper explores the application of Quantum Annealing (QA) to the field of combinatorial optimization. The core idea is to leverage the principles of quantum mechanics, specifically quantum tunneling, to efficiently solve complex optimization problems. We present a framework for utilizing QA to tackle problems represented as quadratic unconstrained binary optimization (QUBO) models. The methodology focuses on mapping the problem's constraints and objective function into a form suitable for QA hardware. We discuss the challenges involved in this mapping process, including the limitations of current QA devices and the need for careful problem formulation. Furthermore, we analyze the potential advantages of QA over classical optimization techniques, particularly concerning the exploration of a wider solution space. The research highlights the promise of QACO as a viable approach for tackling computationally demanding combinatorial optimization tasks, offering a novel method to enhance optimization efficiency.
Quantum optimization shows significant potential in tackling complex combinatorial challenges, prompting our research exploration into the domain of quantum computing to address the Bin Packing Problem (BPP) through Quadratic Unconstrained Binary Optimization (QUBO) formulations. This work-in-progress paper outlines our proposal for a thorough study that encompasses classical bin packing formulations, initiating with the Mixed Integer Linear Program, followed by a comparative analysis of the corresponding QUBO representations, and adopting a structured approach of Quantum Annealing on the D-Wave machine. While our work is still in progress, continuous comparative analyses are being conducted to evaluate various QUBO formulations, derived from diverse classical bin packing models, aiming to identify the most efficient QUBO for addressing the BPP. On another front, efforts are dedicated to refining the solution process further, with a focus on enhancing the precision of the selected QUBO formulation.
Recently, quantum computers have drawn attention to their potential to solve problems faster than classical computers. However, quantum hardware’s limited practicality and scalability have led to an interest in alternative approaches to solving optimization problems. One such approach is classical quantum-inspired annealers, which provide efficient and scalable solutions for combinatorial optimization problems (COPs) using classical hardware. To use annealers, COPs must be formulated as quadratic unconstrained binary optimization (QUBO) forms. Current tools require coding expertise and manual parameter tuning, posing barriers to entry. To address these challenges, we developed a user-friendly software toolchain that offers several advantages. Our toolchain features a friendly input and output interface, an automated parameter tuner, and a library of commonly encountered COPs. Our software toolchain’s accessibility promotes the use of quantum-inspired annealers and accelerates the development of practical solutions for real-world problems.
Recent advances in quantum computing have shown great potential for solving combinatorial optimization problems. A notable example is the quadratic knapsack problem, a constrained binary optimization problem. However, current quantum-inspired algorithms require Quadratic Unconstrained Binary Optimization (QUBO) formulations. Transforming constrained optimization into QUBO impacts the algorithms' speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess them by using Simulated Annealing (SA), and find that three of our approaches outperform existing methods in terms of execution time as well as quality and quantity of feasible solutions found. Also, we tested these transformations on a quantum annealer, which was unable to solve even small instances, due to limitations in connectivity and error rates. In general, our results highlight the advantages of the new approaches, which reduce the number of variables in the QUBO representation.
Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadratic Unconstrained Binary Optimization (QUBO). In this work, we propose PDQUBO ( P erformance- D riven Q uadratic U nconstrained B inary O ptimization), a QUBO-based feature selection method, that is, directly executable on quantum annealers. Unlike prior QUBO-based feature selection approaches on quantum annealers, PDQUBO explicitly quantifies the performance impact of both individual features and feature pairs on recommender system models. This alignment between QUBO optimization objectives and model performance ensures that the solution direction is closely tied to recommendation quality, making it well-suited for practical deployment on quantum hardware. Moreover, by leveraging counterfactual analysis, PDQUBO is model-agnostic and evaluation-metric-independent, making it broadly applicable across diverse recommender architectures and assessment criteria. In addition, we investigate the instability of quantum annealing on real quantum devices with respect to varying problem sizes and problem difficulties. Extensive experiments on real-world datasets demonstrate that PDQUBO consistently outperforms prior QUBO-based feature selection methods on quantum annealers. Furthermore, we compare PDQUBO against classical feature selection baselines on click-through rate (CTR) prediction tasks, showing its strong performance and highlighting the potential of using quantum annealers for real-world feature selection applications. Our findings suggest that integrating quantum optimization with counterfactual analysis provides a promising direction for effective feature selection in recommender systems.
Recent advancements in quantum computing have demonstrated significant potential for solving combinatorial optimization problems, like the quadratic knapsack problem, a constrained binary optimization problem. However, current quantum and quantum-inspired algorithms often require transforming these constrained problems into an unconstrained form, known as Quadratic Unconstrained Binary Optimization (QUBO). Such transformations can significantly impact the algorithms’ speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess all nine methods using Simulated Annealing and find that three of our approaches outperform existing methods in terms of execution time and the quality and quantity of feasible solutions found. Additionally, we tested these transformations on quantum annealers, which were unable to solve even small problem instances, due to limitations in connectivity and error rates. However, our results highlight the advantages of the new approaches, which reduce the total number of variables in the QUBO representation. This is a critical factor for enhanced performance on emerging quantum hardware, since it also reduces the required number of qubits and the embedding chain lengths.