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Accelerating Supercapacitor Lifetime Optimization Using Quantum Machine Learning Surrogates
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DOI：10.1016/j.egyai.2026.100745.png)
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Abstract
En 中文
• Quantum ML surrogates improve supercapacitor cycle-life prediction • Quantum kernels outperform classical models in low-data regimes • Bayesian optimization accelerates lifetime design search • Unified electrochemical and materials dataset enables robust modeling • Experimental validation confirms optimized device performance
Keywords:
Optimization
Supercapacitors
Classic Machine Learning
Quantum Machine Learning
Bayesian Optimization
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