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arXiv:2602.06420

Achieving Sub-Exponential Speedup in Gate-Based Quantum Computing for Quadratic Unconstrained Binary Optimization

Ying-Wei Tseng, Yu-Ting Kao, Yeong-Jar Chang, Jia-Han Ou, Wen-Zhi Zhang

quant-phSubmitted Feb 6, 2026• Announced Feb 9, 2026
v1 · Feb 6, 2026v2 · Feb 9, 2026
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Abstract

Recent quantum-inspired methods based on the Simulated Annealing algorithm have shown strong potential for solving combinatorial optimization problems. However, Grover's algorithm in gate-based quantum computing offers only a quadratic speedup, which remains impractical for large problem sizes. This paper proposes a hybrid approach that integrates Simulated Annealing with Grover's algorithm to achieve sub-exponential speedup, thereby improving its industrial applicability. In enzyme fermentation, variables such as temperature, stirring, wait time, pH, tryptophan, rice flour and others are encoded by 625 binary parameters, defining the space of possible enzyme formulations. We aim to find a binary configuration that maximizes the active ingredient, formulated as a 625-bit Quadratic Unconstrained Binary Optimization problem generated from historical experiments and artificial intelligence techniques. Minimizing the QUBO cost corresponds to maximizing the active ingredient. This case study demonstrates that the proposed hybrid method achieves sub-exponential speedup using gate-based quantum computing.

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12 pages, 6 figures

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