# A black-box optimization method with polynomial-based kernels and quadratic-optimization annealing

arXiv: https://arxiv.org/abs/2501.04225 · HTML: https://mlsanity.com/paper/2501.04225/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Subsampling Factorization Machine Annealing](https://mlsanity.com/paper/2508.08778/) (2025)
- [Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering](https://mlsanity.com/paper/2505.10012/) (2025)
- [Stage-dependent integer-binary encoding in factorization-machine black-box optimization](https://mlsanity.com/paper/2606.23188/) (2026)
- [ILP-BO: Integer Linear Programming-Based Black-Box Optimization](https://mlsanity.com/paper/2609.33131/) (2026)

## Abstract (arXiv)

We introduce kernel-QA, a black-box optimization (BBO) method that constructs surrogate models analytically using low-order polynomial kernels within a quadratic unconstrained binary optimization (QUBO) framework, enabling efficient utilization of Ising machines. The method has been evaluated on artificial landscapes, ranging from uni-modal to multi-modal, with input dimensions extending to 80 for real variables and 640 for binary variables. The results demonstrate that kernel-QA is particularly effective for optimizing black-box functions characterized by local minima and high-dimensional inputs, showcasing its potential as a robust and scalable BBO approach.