Home
For authors
Submission status

Archive
Archive (English)
Current
   Volumes 93-112
   Volumes 113-120
      Volume 120
      Volume 119
      Volume 118
      Volume 117
      Volume 116
      Volume 115
      Volume 114
      Volume 113
Search
VOLUME 118 (2023) | ISSUE 12 | PAGE 938
Tensor train optimization of parameterized quantum circuits
Abstract
We examine a particular realization of derivative-free method as implemented on tensor train based optimization to the variational quantum eigensolver. As an example, we consider parameterized quantum circuits composed of a low-depth hardware-efficient ansatz and Hamiltonian variational ansatz for addressing the ground state of the transverse field Ising model. We further make a comparison with gradient-based optimization techniques and discuss on the advantage of using tensor train based optimization, especially in the presence of noise.