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

• URL: http://arxiv.org/abs/2507.10992v1
• Date: Tue, 15 Jul 2025 05:15:25 GMT
• Title: A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
• Authors: Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly, Stefan M. Wild,
• Abstract summary: ANASTAARS is a noise-aware scalable classical algorithm for variational quantum algorithms.<n>It exploits adaptive random subspace strategies to efficiently optimize the ansatz parameters of a quantum approximate optimization algorithm.
• Score: 0.9086201982977716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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