Tensor train optimization of parametrized quantum circuits

• URL: http://arxiv.org/abs/2306.02024v1
• Date: Sat, 3 Jun 2023 06:50:00 GMT
• Title: Tensor train optimization of parametrized quantum circuits
• Authors: Georgii Paradezhenko, Anastasiia Pervishko, Dmitry Yudin
• Abstract summary: We consider parametrized quantum circuits composed of a low-depth hardware-efficient ansatz and Hamiltonian variational ansatz. We discuss on the advantage of using tensor train based optimization, especially in the presence of noise.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 parametrized 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.

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