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CVaR-Assisted Custom Penalty Function for Constrained Optimization

arXiv Quantum Archived Apr 23, 2026 ✓ Full text saved

arXiv:2604.20088v1 Announce Type: new Abstract: Constrained combinatorial optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models in order to leverage emerging quantum optimization algorithms such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). However, standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties, which can significantly incr

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    Quantum Physics [Submitted on 22 Apr 2026] CVaR-Assisted Custom Penalty Function for Constrained Optimization Xin Wei Lee, Hoong Chuin Lau Constrained combinatorial optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models in order to leverage emerging quantum optimization algorithms such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). However, standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties, which can significantly increase the problem size and distort the optimization landscape. In this work, we propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables and preserves the feasibility structure of the original problem. The proposed approach introduces a nonlinear custom penalty function to enforce inequality constraints directly in the objective function. To address the computational challenges associated with evaluating nonlinear penalties in variational quantum algorithms, we employ the finite-sampling method that avoids the exponential complexity required by exact expectation computation. Furthermore, we integrate the Conditional Value-at-Risk (CVaR) objective to improve optimization robustness and guide the search toward high-quality solutions. The proposed framework is evaluated on instances of the multi-dimensional knapsack problem, a classical benchmark in combinatorial optimization. We showcase that the proposed custom-penalty formulation combined with CVaR sampling achieves improved optimality gaps and more consistent performance compared with conventional slack-based QUBO formulations. The results suggest that careful penalty design can play a critical role in enabling quantum and hybrid quantum-classical algorithms for constrained optimization problems that arise in operations research. Comments: 10 pages, 5 figures Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.20088 [quant-ph]   (or arXiv:2604.20088v1 [quant-ph] for this version)   https://doi.org/10.48550/arXiv.2604.20088 Focus to learn more Submission history From: Xinwei Lee [view email] [v1] Wed, 22 Apr 2026 01:08:38 UTC (90 KB) Access Paper: HTML (experimental) view license Current browse context: quant-ph < prev   |   next > new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Quantum
    Category
    ◌ Quantum Computing
    Published
    Apr 23, 2026
    Archived
    Apr 23, 2026
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