2D Quon Language: Unifying Framework for Cliffords, Matchgates, and Beyond

TL;DR

This paper introduces the 2D Quon language, a unifying diagrammatic framework that captures Clifford and matchgate classes as special cases, enabling efficient representation and analysis of quantum states and processes.

Contribution

The authors develop the 2D Quon language, a universal diagrammatic approach that unifies Clifford and matchgate classes and introduces new tensor network families for quantum computation.

Findings

2D Quon language can represent arbitrary quantum states and dynamics.

Efficient characterization of Clifford and matchgate classes within the Quon framework.

New tensor networks exhibit high entanglement and non-Cliffordness, useful for quantum applications.

Abstract

Simulating generic quantum states and dynamics is practically intractable using classical computers. However, certain special classes -- namely Clifford and matchgate circuits -- permit efficient computation. They provide invaluable tools for studying many-body physics, quantum chemistry, and quantum computation. While both play foundational roles across multiple disciplines, the origins of their tractability seem disparate, and their relationship remain unclear. A deeper understanding of such tractable classes could expand their scope and enable a wide range of new applications. In this work, we make progress toward the unified understanding of the Clifford and matchgate -- these two classes are, in fact, distinct special cases of a single underlying structure. Specifically, we introduce the 2D Quon language, which combines Majorana worldlines with their underlying spacetime topology to diagrammatically represent quantum processes and tensor networks. In full generality, the 2D Quon language is universal -- capable of representing arbitrary quantum states, dynamics, or tensor networks -- yet they become especially powerful in describing Clifford and matchgate classes. Each class can be efficiently characterized in a visually recognizable manner using the Quon framework. This capability naturally gives rise to several families of efficiently computable tensor networks introduced in this work: punctured matchgates, hybrid Clifford-matchgate-MPS, and ansatze generated from factories of tractable networks. All of these exhibit high non-Cliffordness, high non-matchgateness, and large bipartite entanglement entropy. We discuss a range of applications of our approach, from recovering well-known results such as the Kramers-Wannier duality and the star-triangle relation of the Ising model, to enabling variational optimization with novel ansatz states.