7 papers
Rate-Optimal Quantum Discrete Simulation Optimization
Mingjie Hu, Jian-Qiang Hu
Quantum computing has developed rapidly in recent years and has shown potential advantages over classical computing in various domains. In this paper, we study whether quantum comp…
Quantum Monte Carlo Tree Search with Fixed Confidence
Mingjie Hu, Jian-Qiang Hu, Enlu Zhou
Recent advances in quantum computing are opening new opportunities for computationally intensive decision problems. This paper studies how quantum computing can improve Monte Carlo…
The Implementation Cost of Fairness in Service Policy Selection
Junjie Liu, Mingjie Hu, Kejia Hu +2
Service organizations use simulation, pilot studies, and historical data to select service policies that balance aggregate performance and fairness. Existing work primarily evaluat…
Optimal Data Acquisition for Reinforcement Learning: A Large Deviations Perspective
Mingjie Hu, Jian-Qiang Hu, Enlu Zhou
Data acquisition efficiency is a central challenge in deploying reinforcement learning in business and healthcare operations, where interactions are costly, slow, and often involve…
Quantum Grover Adaptive Search for Discrete Simulation Optimization
Mingjie Hu, Jian-qiang Hu, Enlu Zhou
Quantum computing has advanced rapidly in recent years and has shown advantages in a variety of domains. In this paper, we investigate its potential for discrete simulation optimiz…
Adaptive Simulation Experiment for LLM Policy Optimization
Mingjie Hu, Siyang Gao, Jian-qiang Hu +1
Large language models (LLMs) have significant potential to improve operational efficiency in operations management. Deploying these models requires specifying a policy that governs…
Long-Run Conditional Value at Risk Reinforcement Learning
Qixin Wang, Hao Cao, Jian-Qiang Hu +2
We propose a reinforcement learning method for minimizing the long-run conditional value at risk (CVaR) in Markov decision processes with finite state and action spaces. The method…