Year: 2026
Type: article
Abstract: We present Q-SASS, a quasi-Newton method for unconstrained stochastic optimization that does not rely on common random numbers. Most existing quasi-Newton approaches leverage common random numbers to construct second-order updates. However, motivated by challenges in variational quantum algorithms—where such coordination is not possible—we consider the setting in which function values and gradients are accessible only through noisy probabilistic zeroth- and first-order oracles, and no common random numbers can be exploited. We derive high-probability tail bounds on the iteration complexity of our algorithm for nonconvex, convex, and strongly convex (more generally, those satisfying the PL condition) objective functions. Finally, we demonstrate the empirical benefits of our quasi-Newton updating scheme on both synthetic and quantum chemistry problems.
Authors: Matt Menickelly, Stefan M. Wild, Miaolan Xie
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Awards: G102163711, G1286236842, G1799333409, G1863003419, G2287611682, G262962599, Collaborative Research: TRIPODS Institute for Optimization and Learning, G3083819904, G3136028036, G419881185, G42916606, G4501827968, G498139845, G513797252, G5398989114, G6652958823, G6848031779, G6918803902, G878601127, G969889393, G993149817
OpenAlex ID: W7125472286