Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2024
Abstract
Simulation-based optimization is a widely used method to solve stochastic optimization problems. This method aims to identify an optimal solution by maximizing the expected value of the objective function. However, due to its computational complexity, the function cannot be accurately evaluated directly, hence it is estimated through simulation. Exploiting the enhanced efficiency of Quantum Amplitude Estimation (QAE) compared to classical Monte Carlo simulation, it frequently outpaces classical simulation-based optimization, resulting in notable performance enhancements in various scenarios. In this work, we make use of a quantum-enhanced algorithm for simulation-based optimization and apply it to solve a variant of the classical Newsvendor problem which is known to be NP-hard. Such problems provide the building block for supply chain management, particularly in inventory management and procurement optimization under risks and uncertainty.
Keywords
Simulation-based optimization, Quantum amplitude estimation, Quantum-enhanced algorithm, Newsvendor problem
Discipline
Artificial Intelligence and Robotics | Computer Sciences
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE 2024) : Montreal, Quebec, Canada, September 15-20
Identifier
10.1109/QCE60285.2024.00060
Publisher
IEEE
City or Country
Montreal, Canada
Citation
SHARMA, Monit; LAU, Hoong Chuin; and RAYMOND, Rudy.
Quantum-enhanced simulation-based optimization for newsvendor problems. (2024). Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE 2024) : Montreal, Quebec, Canada, September 15-20.
Available at: https://ink.library.smu.edu.sg/sis_research/9982
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/QCE60285.2024.00060
