Barren plateaus in quantum neural network training landscapes

Jarrod R. McClean (), Sergio Boixo (), Vadim N. Smelyanskiy (), Ryan Babbush and Hartmut Neven
Additional contact information
Jarrod R. McClean: Google Inc.
Sergio Boixo: Google Inc.
Vadim N. Smelyanskiy: Google Inc.
Ryan Babbush: Google Inc.
Hartmut Neven: Google Inc.

Nature Communications, 2018, vol. 9, issue 1, 1-6

Abstract: Abstract Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning. Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits. Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. We argue that this is related to the 2-design characteristic of random circuits, and that solutions to this problem must be studied.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
https://www.nature.com/articles/s41467-018-07090-4 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07090-4

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-07090-4

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().