Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus

Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus

Blog Article

In this paper, we propose a general scheme to analyze the gradient vanishing phenomenon, also known as the barren plateau phenomenon, in training quantum neural networks with the ZX-calculus.More precisely, we THYRO SENSE extend the barren plateaus theorem from unitary 2-design circuits to any parameterized quantum circuits under certain reasonable assumptions.The main technical contribution of this paper is representing certain integrations as ZX-diagrams and computing them with the ZX-calculus.

The method is used to analyze four concrete quantum neural networks with different structures.It is shown that, for the hardware efficient ansatz and the MPS-inspired ansatz, there exist barren plateaus, while for Womens Bucket hats the QCNN ansatz and the tree tensor network ansatz, there exists no barren plateau.