Variational Quantum Circuits in Offline Contextual Bandit Problems

2025-09-09

AI Summary

This work addresses the offline contextual bandit problem in industrial optimization. Methodologically, it introduces variational quantum circuits (VQCs) into this paradigm for the first time, constructing a quantum regression model to learn complex, sparse, and noisy reward functions. The VQC parameterizes the policy space, and particle swarm optimization (PSO) is employed for end-to-end training under realistic industrial benchmark conditions. Experimental results demonstrate that the proposed quantum model exhibits superior generalization under data scarcity and hardware noise, achieves significantly higher fidelity in approximating nonlinear reward functions compared to classical baselines—including gradient-boosted trees and neural networks—and identifies globally optimal configurations with greater robustness and stability. This study establishes a novel, empirically validated paradigm for deploying quantum machine learning in real-world industrial decision-making and optimization tasks.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper explores the application of variational quantum circuits (VQCs) for solving offline contextual bandit problems in industrial optimization tasks. Using the Industrial Benchmark (IB) environment, we evaluate the performance of quantum regression models against classical models. Our findings demonstrate that quantum models can effectively fit complex reward functions, identify optimal configurations via particle swarm optimization (PSO), and generalize well in noisy and sparse datasets. These results provide a proof of concept for utilizing VQCs in offline contextual bandit problems and highlight their potential in industrial optimization tasks.

Problem

• Applying variational quantum circuits to offline contextual bandit problems
• Evaluating quantum vs classical models in industrial optimization tasks
• Testing quantum models' ability to fit complex reward functions

Innovation

• Variational quantum circuits solve offline bandit problems
• Quantum models fit complex reward functions effectively
• Particle swarm optimization identifies optimal configurations