Optimization Strategies in Quantum Machine Learning: A Performance Analysis
Source abstract
Abstract
This study presents a comprehensive comparison of multiple optimization algorithms applied to a quantum classification model, utilizing the Cleveland dataset. Specifically, the research focuses on three prominent optimizers—COBYLA, L-BFGS-B, and ADAM—each employing distinct methodologies and widely recognized in the domain of quantum machine learning. The performance of predictive models using these optimizers is rigorously evaluated through key metrics, including accuracy, precision, recall, and F1 score. The findings reveal that the COBYLA optimizer outperforms the L-BFGS-B and ADAM optimizers across all performance metrics, achieving an accuracy of 92%, precision of 89%, recall of 97%, and F1 score of 93%. Furthermore, the COBYLA optimizer exhibits superior computational efficiency, requiring only 1 min of training time compared to 6 min for L-BFGS-B and 10 min for ADAM. These results underscore the critical role played by optimizer selection in enhancing model performance and efficiency in quantum machine learning applications, offering valuable insights for practitioners in the field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Quantum Computing Algorithms and Architecture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Systems, College of Computer and Information Sciences, King Saudi University, Riyadh 11451, Saudi Arabia
Funding
Deanship of Scientific Research at King Saud University
References (47)
Show source-provided references
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