标题： 一种噪声感知的可扩展子空间经典优化器用于量子近似优化算法 显示英文标题

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

摘要： 我们引入了ANASTAARS，这是一种针对变分量子算法（如量子近似优化算法QAOA）的噪声感知可扩展经典优化器。 ANASTAARS利用自适应随机子空间策略，以高效优化QAOA电路的假设参数，旨在解决可能大量QAOA层带来的挑战。 ANASTAARS在通过Johnson--Lindenstrauss变换定义的低维仿射子空间内迭代构建随机插值模型。 这种自适应策略允许选择性地重复使用之前获得的测量结果，显著降低与采样次数获取相关的计算成本。 此外，为了稳健处理噪声测量，ANASTAARS通过估计噪声幅度并相应调整信任区域步长，结合了噪声感知优化技术。 数值实验展示了所提出方法在近期量子计算应用中的实际可扩展性。 显示英文摘要

摘要： 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.