Analyzing variational quantum landscapes with information content

Abstract

The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work we investigate such landscapes through the lens of information content, a measure of the variability between points in parameter space. Our major contribution connects the information content to the average norm of the gradient, for which we provide robust analytical bounds on its estimators. This result holds for any (classical or quantum) variational landscape. We validate the analytical understating by numerically studying the scaling of the gradient in an instance of the barren plateau problem. In such instance we are able to estimate the scaling pre-factors in the gradient. Our work provides a new way to analyze variational quantum algorithms in a data-driven fashion well-suited for near-term quantum computers.

arXiv comment8 pages + 6 pages appendix + 2 pages references, 5 figures, 6 tables. Peer-reviewed version published in npj quantum information

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Cited by in corpus (6)

• Barren Plateaus in Variational Quantum Computing
• Analyzing variational quantum landscapes with information content
• Resource frugal optimizer for quantum machine learning
• Gradients and frequency profiles of quantum re-uploading models
• Variational Quantum Subspace Construction via Symmetry-Preserving Cost Functions
• Variational Quantum Algorithm Landscape Reconstruction by Low-Rank Tensor Completion