Cost function dependent barren plateaus in shallow parametrized quantum circuits

M Cerezo; Akira Sone; Tyler Volkoff; Lukasz Cincio; Patrick J Coles

doi:10.1038/s41467-021-21728-w

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Nat Commun. 2021 Mar 19;12(1):1791. doi: 10.1038/s41467-021-21728-w.

Authors

M Cerezo^{1

2}, Akira Sone^{3

4}, Tyler Volkoff³, Lukasz Cincio³, Patrick J Coles⁵

Affiliations

¹ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. cerezo@lanl.gov.
² Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA. cerezo@lanl.gov.
³ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁴ Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA.
⁵ Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA. pcoles@lanl.gov.

Abstract

Variational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is [Formula: see text]. Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.