Speedy Contraction of ZX Diagrams with Triangles via Stabiliser Decompositions

TL;DR

This paper introduces a novel method for faster classical simulation of quantum circuits using ZX calculus with triangle operations, enabling efficient contraction of diagrams and detection of barren plateaus in quantum machine learning.

Contribution

We develop a new stabiliser decomposition technique involving triangles in ZX diagrams, significantly accelerating quantum circuit simulation and gradient variance analysis.

Findings

Achieved up to multiple orders of magnitude speed-up in circuit simulation.

Demonstrated effective detection of barren plateau phenomena in quantum neural networks.

Benchmarked against tensor network methods showing competitive performance.

Abstract

Recent advances in classical simulation of Clifford+T circuits make use of the ZX calculus to iteratively decompose and simplify magic states into stabiliser terms. We improve on this method by studying stabiliser decompositions of ZX diagrams involving the triangle operation. We show that this technique greatly speeds up the simulation of quantum circuits involving multi-controlled gates which can be naturally represented using triangles. We implement our approach in the QuiZX library and demonstrate a significant simulation speed-up (up to multiple orders of magnitude) for random circuits and a variation of previously used benchmarking circuits. Furthermore, we use our software to contract diagrams representing the gradient variance of parametrised quantum circuits, which yields a tool for the automatic numerical detection of the barren plateau phenomenon in ans\"atze used for quantum machine learning. Compared to traditional statistical approaches, our method yields exact values for gradient variances and only requires contracting a single diagram. The performance of this tool is competitive with tensor network approaches, as demonstrated with benchmarks against the quimb library.