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arXiv · 2306.02024

Tensor train optimization of parametrized quantum circuits

Georgii Paradezhenko·Anastasiia Pervishko·Dmitry Yudin
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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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Georgii Paradezhenko, Anastasiia Pervishko, Dmitry Yudin. 2023-06-03. Tensor train optimization of parametrized quantum circuits. https://arxiv.org/abs/2306.02024

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