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While Large Language Models (LLMs) have shown exceptional generalization capabilities, their ability to process graph data, such as molecular structures, remains limited. To bridge this gap, this paper proposes Graph2Token, an efficient…

Machine Learning · Computer Science 2025-03-11 Runze Wang , Mingqi Yang , Yanming Shen

The ZX calculus is a mathematical tool to represent and analyse quantum operations by manipulating diagrams which in effect represent tensor networks. Two families of nodes of these networks are ones which commute with either Z rotations or…

Quantum Physics · Physics 2021-09-07 Niel de Beaudrap

Compiling quantum circuits to account for hardware restrictions is an essential part of the quantum computing stack. Circuit compilation allows us to adapt algorithm descriptions into a sequence of operations supported by real quantum…

Quantum Physics · Physics 2025-10-14 Alejandro Villoria , Henning Basold , Alfons Laarman

We introduce a class of rooted graphs which allows one to encode various kinds of classical or quantum circuits. We then follow a set-theoretic approach to define rewrite systems over the considered graphs and propose a new complete…

Logic in Computer Science · Computer Science 2021-03-23 Rachid Echahed , Mnacho Echenim , Mehdi Mhalla , Nicolas Peltier

We introduce the first complete and approximatively universal diagrammatic language for quantum mechanics. We make the ZX-Calculus, a diagrammatic language introduced by Coecke and Duncan, complete for the so-called Clifford+T quantum…

Quantum Physics · Physics 2018-02-26 Emmanuel Jeandel , Simon Perdrix , Renaud Vilmart

Due to the rapid development of quantum computing, the compact representation of quantum operations based on decision diagrams has been received more and more attraction. Since variable orders have a significant impact on the size of the…

Quantum Physics · Physics 2022-07-26 Yonghong Li , Hao Miao

Large language models (LLMs) are being increasingly explored for graph tasks. Despite their remarkable success in text-based tasks, LLMs' capabilities in understanding explicit graph structures remain limited, particularly with large…

Machine Learning · Computer Science 2024-10-31 Sambhav Khurana , Xiner Li , Shurui Gui , Shuiwang Ji

Using Large Language Models (LLMs) to process graph-structured data is an active research area, yet current state-of-the-art approaches typically rely on multi-step pipelines with Graph Neural Network (GNN) encoders that compress rich…

Machine Learning · Computer Science 2026-05-12 Dario Vajda

We present here our ongoing work on a Domain Specific Language which aims to simplify Monte-Carlo simulations and measurements in the domain of Lattice Quantum Chromodynamics. The tool-chain, called Qiral, is used to produce…

The ZX-calculus is a powerful framework for reasoning in quantum computing. It provides in particular a compact representation of matrices of interests. A peculiar property of the ZX-calculus is the absence of a formal sum allowing the…

Quantum Physics · Physics 2024-08-07 Emmanuel Jeandel , Simon Perdrix , Margarita Veshchezerova

The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of developing a graph foundation model that generalizes diverse…

Computation and Language · Computer Science 2026-03-03 Zhongjian Zhang , Xiao Wang , Mengmei Zhang , Jiarui Tan , Chuan Shi

We introduce diagrammatic differentiation for tensor calculus by generalising the dual number construction from rigs to monoidal categories. Applying this to ZX diagrams, we show how to calculate diagrammatically the gradient of a linear…

Quantum Physics · Physics 2021-09-21 Alexis Toumi , Richie Yeung , Giovanni de Felice

ZX-calculus is a high-level graphical formalism for qubit computation. In this paper we give the ZX-rules that enable one to derive all equations between 2-qubit Clifford+T quantum circuits. Our rule set is only a small extension of the…

Quantum Physics · Physics 2018-06-13 Bob Coecke , Quanlong Wang

Large language models (LLMs) have demonstrated immense potential across various tasks. However, research for exploring and improving the capabilities of LLMs in interpreting graph structures remains limited. To address this gap, we conduct…

Computation and Language · Computer Science 2025-02-17 Jie He , Yijun Yang , Wanqiu Long , Deyi Xiong , Victor Gutierrez-Basulto , Jeff Z. Pan

Vector Quantization (VQ) has recently emerged as a promising approach for learning discrete representations of graph-structured data. However, a fundamental challenge, i.e., codebook collapse, remains underexplored in the graph domain,…

Machine Learning · Computer Science 2025-09-29 Zian Zhai , Fan Li , Xingyu Tan , Xiaoyang Wang , Wenjie Zhang

In the process of digital transformation, enterprises are faced with problems such as insufficient semantic understanding of unstructured data and lack of intelligent decision-making basis in driving mechanisms. This study proposes a method…

Artificial Intelligence · Computer Science 2026-01-09 Huayi Liu

Visual question answering (VQA) in medical imaging aims to support clinical diagnosis by automatically interpreting complex imaging data in response to natural language queries. Existing studies typically rely on distinct visual and textual…

Computer Vision and Pattern Recognition · Computer Science 2025-07-08 Yuanhe Tian , Chen Su , Junwen Duan , Yan Song

Mapping fermionic systems to qubits on a quantum computer is often the first step for algorithms in quantum chemistry and condensed matter physics. However, it is difficult to reconcile the many different approaches that have been proposed,…

Quantum Physics · Physics 2025-05-12 Haytham McDowall-Rose , Razin A. Shaikh , Lia Yeh

The ZH-calculus is a complete graphical calculus for linear maps between qubits that admits a straightforward encoding of hypergraph states and circuits arising from the Toffoli+Hadamard gate set. In this paper, we establish a…

Quantum Physics · Physics 2021-09-07 Louis Lemonnier , John van de Wetering , Aleks Kissinger

Large Language Models (LLMs) have shown remarkable capabilities in processing various data structures, including graphs. While previous research has focused on developing textual encoding methods for graph representation, the emergence of…

Machine Learning · Computer Science 2024-09-16 Zhiqiang Zhong , Davide Mottin
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