A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Abstract
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lindenstrauss transforms. This adaptive strategy allows the selective reuse of previously acquired measurements, significantly reducing computational costs associated with shot acquisition. Furthermore, to robustly handle noisy measurements, ANASTAARS incorporates noise-aware optimization techniques by estimating noise magnitude and adjusts trust-region steps accordingly. Numerical experiments demonstrate the practical scalability of the proposed method for near-term quantum computing applications.
Cite
@article{arxiv.2507.10992,
title = {A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm},
author = {Kwassi Joseph Dzahini and Jeffrey M. Larson and Matt Menickelly and Stefan M. Wild},
journal= {arXiv preprint arXiv:2507.10992},
year = {2025}
}