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arXiv:2506.14129

A Quantum Annealing Approach for Solving Optimal Feature Selection and Next Release Problems

Shuchang Wang, Xiaopeng Qiu, Yanbing Xue, Yan‐Fu Li, Wei Yang

Published Jun 17, 2025• 0 citations
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Abstract

Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leveraging the computational potential of quantum systems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems, we employ a decomposition strategy guided by maximum energy impact (MEI), integrating QA with a steepest descent method to enhance local search efficiency. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II and the ILP-based $ε$-constraint method. Experimental results reveal that while our methods produce fewer non-dominated solutions than $ε$-constraint, they achieve significant reductions in execution time. Moreover, compared to NSGA-II, our methods deliver more non-dominated solutions with superior computational efficiency. These findings underscore the potential of QA in advancing scalable and efficient solutions for SBSE challenges.

DOI
10.48550/arxiv.2506.14129

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Search-based software engineering (SBSE) addresses critical optimization challenges in software engineering, including the next release problem (NRP) and feature selection problem (FSP). While traditional heuristic approaches and integer linear programming (ILP) methods have demonstrated efficacy for small to medium-scale problems, their scalability to large-scale instances remains unknown. Here, we introduce quantum annealing (QA) as a subroutine to tackling multi-objective SBSE problems, leveraging the computational potential of quantum systems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems, we employ a decomposition strategy guided by maximum energy impact (MEI), integrating QA with a steepest descent method to enhance local search efficiency. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II and the ILP-based εεε-constraint method. Experimental results reveal that while our methods produce fewer non-dominated solutions than εεε-constraint, they achieve significant reductions in execution time. Moreover, compared to NSGA-II, our methods deliver more non-dominated solutions with superior computational efficiency. These findings underscore the potential of QA in advancing scalable and efficient solutions for SBSE challenges.
Search-based software engineering (SBSE) tackles critical optimization problems in software engineering, including the next release problem (NRP) and feature selection problem (FSP). Traditional heuristic approaches and integer linear programming (ILP) methods work well for small- to medium-scale problems but face growing computational cost as instances scale up. We investigate quantum annealing (QA) as an optimization subroutine for multi-objective SBSE problems. We propose two QA-based algorithms tailored to different problem scales. For small-scale problems, we reformulate multi-objective optimization (MOO) as single-objective optimization (SOO) using penalty-based mappings for quantum processing. For large-scale problems that exceed current hardware capacity, we employ a decomposition strategy guided by maximum energy impact (MEI) that partitions the problem into smaller sub-QUBOs, integrating QA with a steepest-descent method for local search. Applied to NRP and FSP, our approaches are benchmarked against the heuristic NSGA-II, IBEA, and MOEA/D, as well as the ILP-based ϵ-constraint method. The experimental results reveal that while our methods produce fewer non-dominated solutions than ϵ-constraint, they achieve substantial reductions in execution time. Compared to the evolutionary baselines, our methods achieve competitive solution quality with lower runtime on the instances that they can encode. The penalty-based QUBO formulation fails to reach feasible regions on constraint-dense FSP instances, limiting the current applicability of the approach. QA is a promising but still hardware-limited component for multi-objective SBSE workflows, rather than a wholesale replacement for classical solvers.
Quantum computing, particularly in the area of quantum optimization, is steadily progressing toward practical applications, supported by an expanding range of hardware platforms and simulators. While Software Engineering (SE) optimization has a strong foundation, which is exemplified by the active Search-Based Software Engineering (SBSE) community and numerous classical optimization methods, the growing complexity of modern software systems and their engineering processes demands innovative solutions. This Systematic Literature Review (SLR) focuses specifically on studying the literature that applies quantum or quantum-inspired algorithms to solve classical SE optimization problems. We examine 76 primary studies selected from an initial pool of 2083 publications obtained through systematic searches of six digital databases using carefully crafted search strings. Our findings reveal concentrated research efforts in areas such as SE operations and software testing, while exposing significant gaps across other SE activities. Additionally, the SLR uncovers relevant works published outside traditional SE venues, underscoring the necessity of this comprehensive review. Overall, our study provides a broad overview of the research landscape, empowering the SBSE community to leverage quantum advancements in addressing next-generation SE challenges.
The Search Based Software Engineering (SBSE) is widely used in software engineering for identifying optimal solutions. However, there is no polynomial-time complexity solution used in the traditional algorithms for SBSE, and that causes the cost very high. In this paper, we analyze and compare several quantum search algorithms that could be applied for SBSE: quantum adiabatic evolution searching algorithm, fixed-point quantum search (FPQS), quantum walks, and a rapid modified Grover quantum searching method. The Grover’s algorithm is thought as the best choice for a large-scaled unstructured data searching and theoretically it can be applicable to any search-space structure and any type of searching problems.
Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP’s bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly’s classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, provide foundational insights towards leveraging QA in Process Systems Engineering.
Quantum-Guided Constraint Optimization (QGCO) presents a novel approach to solving complex optimization problems by integrating quantum annealing with constraint optimization techniques. This paper explores the potential of leveraging quantum annealing to accelerate the search process while simultaneously enforcing constraints, resulting in enhanced solution quality. We delve into the theoretical framework, algorithmic implementation, and preliminary results demonstrating the efficacy of this method. The core claim focuses on the synergistic combination of quantum annealing and constraint optimization to achieve a significant improvement in optimization speed and solution quality compared to traditional methods. This research addresses limitations of existing techniques by providing a robust and efficient framework for tackling challenging optimization problems across diverse domains.
We investigate the use of quantum computing algorithms on real quantum hardware to tackle the computationally intensive task of feature selection for light-weight medical image datasets.Feature selection is often formulated as a k of n selection problem, where the complexity grows binomially with increasing k and n.Quantum computers, particularly quantum annealers, are well-suited for such problems, which may offer advantages under certain problem formulations.We present a method to solve larger feature selection instances than previously demonstrated on commercial quantum annealers.Our approach combines a linear Ising penalty mechanism with subsampling and thresholding techniques to enhance scalability.The method is tested in a toy problem where feature selection identifies pixel masks used to reconstruct small-scale medical images.We compare our approach against a range of feature selection strategies, including randomized baselines, classical supervised and unsupervised methods, combinatorial optimization via classical and quantum solvers, and learning-based feature representations.The results indicate that quantum annealing-based feature selection is effective for this simplified use case, demonstrating its potential in high-dimensional optimization tasks.However, its applicability to broader, real-world problems remains uncertain, given the current limitations of quantum computing hardware.While learned feature representations such as autoencoders achieve superior reconstruction performance, they do not offer the same level of interpretability or direct control over input feature selection as our approach.