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Characterizing the loss landscape of variational quantum circuits · Around

2020

Characterizing the loss landscape of variational quantum circuits

Huembeli, Patrick, Dauphin, Alexandre

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Machine learning techniques enhanced by noisy intermediate-scale quantum (NISQ) devices and especially variational quantum circuits (VQC) have recently attracted much interest and have already been benchmarked for certain problems.

Inspired by classical deep learning, VQCs are trained by gradient descent methods which allow for efficient training over big parameter spaces.
For NISQ sized circuits, such methods show good convergence.
There are however still many open questions related to the convergence of the loss function and to the trainability of these circuits in situations of vanishing gradients.

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