Surrogate-Guided Quantum Discovery in Black-Box Landscapes with Latent-Quadratic Interaction Embedding Transformers

2026-02-10

AI Summary

This work addresses the challenge of simultaneously achieving high utility and structural diversity in configuration search under black-box evaluation with limited query budgets. The authors propose a novel approach that integrates self-attention mechanisms with the Quantum Approximate Optimization Algorithm (QAOA). By leveraging self-attention to model higher-order variable dependencies and projecting them into a positive semidefinite quadratic form, they construct a surrogate Hamiltonian compatible with QAOA. This formulation uniquely incorporates higher-order interaction structure learning into a quantum optimization framework, overcoming the pairwise interaction limitation inherent in traditional factorization machines. Evaluated on an enterprise document risk discovery task, the method nearly doubles the number of detected tail-risk anomalies compared to classical approaches and uncovers a substantial set of non-overlapping, high-utility configurations.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Discovering configurations that are both high-utility and structurally diverse under expensive black-box evaluation and strict query budgets remains a central challenge in data-driven discovery. Many classical optimizers concentrate on dominant modes, while quality-diversity methods require large evaluation budgets to populate high-dimensional archives. Quantum Approximate Optimization Algorithm (QAOA) provides distributional sampling but requires an explicit problem Hamiltonian, which is unavailable in black-box settings. Practical quantum circuits favor quadratic Hamiltonians since higher-order interaction terms are costly to realize. Learned quadratic surrogates such as Factorization Machines (FM) have been used as proxies, but are limited to pairwise structure. We extend this surrogate-to-Hamiltonian approach by modelling higher-order variable dependencies via self-attention and projects them into a valid Positive Semi-Definite quadratic form compatible with QAOA. This enables diversity-oriented quantum sampling from learned energy landscapes while capturing interaction structure beyond pairwise terms. We evaluate on risk discovery for enterprise document processing systems against diverse classical optimizers. Quantum-guided samplers achieve competitive utility while consistently improving structural diversity and exclusive discovery. FM surrogates provide stronger early coverage, whereas ours yields higher-fidelity surrogate landscapes and better extreme-case discovery. Our method recovers roughly twice as many structurally tail-risk outliers as most classical baselines and identify an exclusive non-overlapping fraction of high-utility configurations not found by competing methods, highlighting that an effective mechanism for learning higher-order interaction structure and projecting it into quadratic surrogate Hamiltonians for quantum-assisted black-box discovery.

Problem

• black-box optimization
• quality-diversity
• quantum discovery
• surrogate modeling
• structural diversity

Innovation

• Latent-Quadratic Interaction Embedding
• Self-Attention Surrogate
• Quantum-Assisted Black-Box Optimization
• Higher-Order Interaction Modeling
• QAOA-Compatible Hamiltonian