在具有潜在二次交互嵌入变换的黑盒景观中进行代理引导的量子发现
Surrogate-Guided Quantum Discovery in Black-Box Landscapes with Latent-Quadratic Interaction Embedding Transformers
AI总结：
本文提出了一种基于潜在二次交互嵌入变换的量子引导方法，通过自注意力机制建模高阶变量依赖，提升黑盒发现中的结构多样性和实用性。
摘要：
在昂贵的黑盒评估和严格的查询预算下发现同时具有高实用性且结构多样化的配置仍然是数据驱动发现中的核心挑战。许多经典优化器专注于主导模式，而质量-多样性方法需要大量的评估预算来填充高维档案。量子近似优化算法（QAOA）提供了分布采样，但需要一个显式的问题哈密顿量，这在黑盒设置中不可用。实用的量子电路倾向于二次哈密顿量，因为更高阶的相互作用项成本较高。已学习的二次代理，如因子机（FM），已被用作代理，但局限于成对结构。我们通过自注意力机制建模更高阶的变量依赖关系，并将它们投影到有效的正定二次形式中，以与QAOA兼容。这使得能够在学习的能量景观中进行以多样性为导向的量子采样，同时捕捉超越成对项的相互作用结构。我们在企业文档处理系统中的风险发现任务上进行了评估，与多种经典优化器进行比较。量子引导的采样器在实用性方面具有竞争力，同时在结构多样性和独家发现方面始终有所提高。FM代理提供了更强的早期覆盖，而我们的方法则产生了更高保真度的代理景观和更好的极端情况发现。我们的方法恢复了大约是大多数经典基线的两倍结构尾风险异常值，并识别出一个不重叠的高实用性配置比例，这些配置未被竞争方法发现，这表明学习更高阶的相互作用结构并将其投影到二次代理哈密顿量中，以辅助量子辅助的黑盒发现，是一个有效的机制。
英文摘要：
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.
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