@techreport{oai:ipsj.ixsq.nii.ac.jp:00240374,
 author = {Shigetora, Miyashita and Yukio, Kawashima and Hitomi, Takahashi and Hiroshi, Horii and Shigetora, Miyashita and Yukio, Kawashima and Hitomi, Takahashi and Hiroshi, Horii},
 issue = {9},
 month = {Oct},
 note = {We present a novel workflow for sample-based quantum diagonalization (SQD) that combines classical optimization and quantum-selected configuration interaction (QSCI) with configuration recovery. Although quantum computers hold promise in simulating many-body quantum systems, most algorithms, including the variational quantum eigensolver (VQE), are limited by circuit depth and high error rates. To address these challenges, we introduce an implementation of SQD with constrained optimization by linear approximation (COBYLA) optimizer. This paper discusses the runtime of classical resources for performing SQD, its optimization with COBYLA for a small molecule, and the computational time required to simulate larger molecules., We present a novel workflow for sample-based quantum diagonalization (SQD) that combines classical optimization and quantum-selected configuration interaction (QSCI) with configuration recovery. Although quantum computers hold promise in simulating many-body quantum systems, most algorithms, including the variational quantum eigensolver (VQE), are limited by circuit depth and high error rates. To address these challenges, we introduce an implementation of SQD with constrained optimization by linear approximation (COBYLA) optimizer. This paper discusses the runtime of classical resources for performing SQD, its optimization with COBYLA for a small molecule, and the computational time required to simulate larger molecules.},
 title = {Performance Analysis of Sample-Based Quantum Diagonalization},
 year = {2024}
}