Adaptive measurement strategy for quantum subspace methods

• DOI: 10.1088/1367-2630/ad2c3b
• Publication Date: 2024-02-22T22:27:01Z
• Authors: Yuma Nakamura; Yoshichika Yano; Nobuyuki Yoshioka
• Keywords: observable estimation; 0301 basic medicine; Quantum Physics; Science; Physics; QC1-999; Q; FOS: Physical sciences; quantum computing; 03 medical and health sciences; classical shadows; Quantum Physics (quant-ph); quantum subspace expansion; partial tomography

Abstract Estimation of physical observables for unknown quantum states is an important problem that underlies a wide range fields, including information processing, physics, and chemistry. In the context computation, in particular, existing studies have mainly focused on holistic state tomography or estimation specific with known classical descriptions, while this lacks class problems where target itself relies measurement outcome. work, we propose adaptive optimization method useful subspace methods, namely variational simulation methods utilize postprocessing outcomes. The proposed first determines protocol classically simulatable states, then adaptively updates expansion (QSE) according to result. As numerical demonstration, shown excited-state molecules (i) are able reduce number measurements by order magnitude constructing appropriate strategy (ii) iteration converges successfully even strongly correlated molecule H 4 . Our work reveals potential QSE can be empowered elaborated protocols, opens path further pursue efficient techniques practical computations.