Approximate Quantum State Preparation with Tree-Based Bayesian Optimization Surrogates

ICLR 2026 Conference Submission14535 Authors

Keywords: Quantum Computing, Bayesian Optimization, Approximate State Preparation, Tree-based Surrogates

TL;DR: CircuitTree is a layerwise tree-based optimization strategy to enable distributed and sample-efficient approximate state preparation with theoretical convergence guarantees.

Abstract: We study the problem of approximate state preparation on near-term quantum computers, where the goal is to construct a parameterized circuit that reproduces the output distribution of a target quantum state while minimizing resource overhead. This task is especially relevant for near-term algorithms where distributional matching suffices, but it is challenging due to stochastic outputs, limited circuit depth, and a high-dimensional, non-smooth parameter space. We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models, which avoids the scalability and smoothness assumptions of Gaussian Process surrogates. Our framework introduces a structured layerwise decomposition strategy that partitions parameters into blocks aligned with variational circuit architecture, enabling distributed and sample-efficient optimization with theoretical convergence guarantees. Empirical evaluations on synthetic benchmarks and variational tasks validate our theoretical insights, showing that CircuitTree achieves low total variation distance and high fidelity while requiring significantly shallower circuits than existing approaches.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 14535