Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization

Abstract

We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a target distribution while minimizing resource overhead. This task arises naturally in hybrid quantum-classical workflows, where measurement-driven objectives replace full state reconstruction, and is central to applications in generative modeling and variational inference. However, it remains challenging due to hardware noise, limited circuit depth, and a high-dimensional, non-convex parameter space. We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models for scalable, domain-aware distribution matching. Our approach introduces a structured, layerwise decomposition aligned with the variational circuit architecture, enabling distributed and sample-efficient optimization within hybrid loops with theoretical convergence guarantees. Across representative distribution-matching tasks, CircuitTree achieves up to 2-3 times lower total variation distance while using 40-60% fewer gates than prior approaches. These results demonstrate its effectiveness as a practical building block for end-to-end hybrid quantum sampling.