A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm

TL;DR

ANASTAARS is a noise-aware, scalable classical optimizer for QAOA that uses adaptive subspace strategies and noise estimation to efficiently optimize parameters in quantum algorithms, reducing measurement costs and improving robustness.

Contribution

It introduces ANASTAARS, a novel noise-aware optimizer employing adaptive subspace methods and measurement reuse for scalable QAOA parameter optimization.

Findings

Demonstrates scalability for near-term quantum applications

Reduces measurement costs through adaptive subspace reuse

Handles noisy measurements effectively

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.