Conservative quantum offline model-based optimization

2025-06-24

AI Summary

This work addresses offline model-based black-box optimization—where only a fixed, pre-collected dataset is available, active experimentation is prohibited, and over-optimistic extrapolation of the surrogate model into unexplored regions must be suppressed. We propose COM-QEL, a novel algorithm that integrates variational quantum circuits with quantum extreme learning (QEL) and incorporates Conservative Objective Modeling (COM) regularization. COM explicitly constrains the predicted upper bound of the objective, mitigating out-of-distribution generalization bias while preserving the expressive power of quantum models. Crucially, COM-QEL operates entirely offline, requiring no online function queries. On standard benchmarks, it consistently converges to solutions achieving higher true objective values than baseline QEL, demonstrating significant improvements in both reliability and optimization performance. These results underscore the critical role of conservative modeling in enhancing the robustness and efficacy of offline quantum-assisted optimization.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experimentation. Recent work has introduced quantum extremal learning (QEL), which leverages the expressive power of variational quantum circuits to learn accurate surrogate functions by training on a few data points. However, as widely studied in the classical machine learning literature, predictive models may incorrectly extrapolate objective values in unexplored regions, leading to the selection of overly optimistic solutions. In this paper, we propose integrating QEL with conservative objective models (COM) - a regularization technique aimed at ensuring cautious predictions on out-of-distribution inputs. The resulting hybrid algorithm, COM-QEL, builds on the expressive power of quantum neural networks while safeguarding generalization via conservative modeling. Empirical results on benchmark optimization tasks demonstrate that COM-QEL reliably finds solutions with higher true objective values compared to the original QEL, validating its superiority for offline design problems.

Problem

• Optimizing black-box functions using fixed prior data
• Preventing overly optimistic solutions in unexplored regions
• Combining quantum learning with conservative modeling

Innovation

• Combines quantum extremal learning with conservative models
• Uses variational quantum circuits for surrogate functions
• Ensures cautious predictions via regularization technique