Barren Plateaus as Destructive Interference: A Diagnostic Framework and Implications for Structured Ansatzes
Pilsung Kang

TL;DR
This paper introduces a diagnostic framework to understand barren plateaus in quantum neural networks as destructive interference among gradient contributions, revealing differences between ansatzes and connecting to variance-based theories.
Contribution
It presents a new interference-based perspective on barren plateaus, with diagnostic tools and analysis of different ansatzes, linking destructive interference to gradient suppression mechanisms.
Findings
Haar-random sign cancellation regime identified with stable interference measure
HEA remains near the random-sign cancellation regime across sizes and depths
HVA shows larger interference measures due to sign organization, not just term count
Abstract
Barren plateaus (BPs) are usually described by the exponential suppression of gradient variance, but the mechanism by which gradient signal disappears remains unclear. We show that this phenomenon can be understood as destructive interference among termwise gradient contributions. To make this perspective operational, we introduce a diagnostic framework based on the cancellation ratio , the effective term count , and the interference-quality measure . Under a random-sign model, remains near a stable baseline, defining a random-sign cancellation regime. For the transverse-field Ising model (TFIM), we find that the hardware-efficient ansatz (HEA) remains close to this regime across system sizes and depths, whereas the Hamiltonian variational ansatz (HVA) systematically escapes it. In particular,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
