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.

References in corpus (15)

• Variational Quantum Algorithms
• The theory of variational hybrid quantum-classical algorithms
• Noisy intermediate-scale quantum (NISQ) algorithms
• A Review on Quantum Approximate Optimization Algorithm and its Variants
• Derivative-free optimization methods
• Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View
• Variational Quantum Linear Solver
• An Adaptive Optimizer for Measurement-Frugal Variational Algorithms
• Classical Optimizers for Noisy Intermediate-Scale Quantum Devices
• A Comparison of Various Classical Optimizers for a Variational Quantum Linear Solver
• Parameter Transfer for Quantum Approximate Optimization of Weighted MaxCut
• Multistart Methods for Quantum Approximate Optimization
• Latency considerations for stochastic optimizers in variational quantum algorithms
• Stochastic trust-region algorithm in random subspaces with convergence and expected complexity analyses
• Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates