{"ID":2894242,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10992","arxiv_id":"2507.10992","title":"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.","short_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...","url_abs":"https://arxiv.org/abs/2507.10992","url_pdf":"https://arxiv.org/pdf/2507.10992v1","authors":"[\"Kwassi Joseph Dzahini\",\"Jeffrey M. Larson\",\"Matt Menickelly\",\"Stefan M. Wild\"]","published":"2025-07-15T05:15:25Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"math.OC\"]","methods":"[]","has_code":false}