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We study, from a combinatorial viewpoint, the quantized coordinate ring of mxn matrices over an infinite field K (also called quantum matrices) and its torus-invariant prime ideals. The first part of this paper shows that this algebra,…

Quantum Algebra · Mathematics 2016-01-20 Karel Casteels

We undertake a study of the notion of a quantum graph over arbitrary finite-dimensional $C^*$-algebras $B$ equipped with arbitrary faithful states. Quantum graphs are realised principally as either certain operators on $L^2(B)$, the quantum…

Operator Algebras · Mathematics 2024-11-27 Matthew Daws

Graph states are used to represent mathematical graphs as quantum states on quantum computers. They can be formulated through stabilizer codes or directly quantum gates and quantum states. In this paper we show that a quantum graph neural…

Quantum Physics · Physics 2024-10-31 Ammar Daskin

We present a mathematical framework that unifies the quantum causal history formalism from theoretical high energy physics and the directed graph operator framework from the theory of operator algebras. The approach involves completely…

Operator Algebras · Mathematics 2007-05-23 David W. Kribs

Molecular graphs generally contain subgraphs (known as groups) that are identifiable and significant in composition, functionality, geometry, etc. Flat latent representations (node embeddings or graph embeddings) fail to represent, and…

Machine Learning · Computer Science 2019-04-05 Daniel T. Chang

We provide a graphical method to describe and analyze non-Gaussian quantum states using a hypergraph framework. These states are pivotal resources for quantum computing, communication, and metrology, but their characterization is hindered…

Quantum Physics · Physics 2025-07-28 Lina Vandré , Boxuan Jing , Yu Xiang , Otfried Gühne , Qiongyi He

This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges.…

Machine Learning · Computer Science 2019-05-16 Bruce MacLennan

Pseudo-unitary circuits are recurring in both S-matrix theory and analysis of No-Go theorems. We propose a matrix and diagrammatic representation for the operation that maps S-matrices to T-matrices and, consequently, a unitary group to a…

High Energy Physics - Theory · Physics 2025-12-04 Dennis Lima , Saif Al-Kuwari

We argue that the quantum-theoretical structures studied in several recent lines of research cannot be adequately described within the standard framework of quantum circuits. This is in particular the case whenever the combination of…

Quantum Physics · Physics 2021-07-14 Augustin Vanrietvelde , Hlér Kristjánsson , Jonathan Barrett

We derive a framework to apply topological quantum chemistry in systems subject to magnetic flux. We start by deriving the action of spatial symmetry operators in a uniform magnetic field, which extends Zak's magnetic translation groups to…

Mesoscale and Nanoscale Physics · Physics 2023-06-19 Yuan Fang , Jennifer Cano

We introduce a mapping between graphs and pure quantum bipartite states and show that the associated entanglement entropy conveys non-trivial information about the structure of the graph. Our primary goal is to investigate the family of…

Quantum Physics · Physics 2012-02-24 Silvano Garnerone , Paolo Giorda , Paolo Zanardi

Within the general context of the architecture in quantum computer design, this paper aims is to provide a general strategy to obtain a block-matrix representation of quantum gates applied to qubits placed in arbitrary positions over an…

Quantum Physics · Physics 2017-11-28 Giuseppe Sergioli

Networks constitute efficient tools for assessing universal features of complex systems. In physical contexts, classical as well as quantum, networks are used to describe a wide range of phenomena, such as phase transitions, intricate…

Quantum Physics · Physics 2016-01-22 Jaroslav Novotný , Gernot Alber , Igor Jex

Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed…

Social and Information Networks · Computer Science 2015-05-29 Jose F. Rodrigues , Hanghang Tong , Jia-Yu Pan , Agma J. M. Traina , Caetano Traina , Christos Faloutsos

Quantum graphs have been introduced by Duan, Severini, and Winter to describe the zero-error behaviour of quantum channels. Since then, quantum graph theory has become a field of study in its own right. A substantial source of difficulty in…

Operator Algebras · Mathematics 2026-04-20 Gian Luca Spitzer , Ion Nechita

Graph structures are ubiquitous throughout the natural sciences. Here we consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks. In particular, we consider training data…

Quantum Physics · Physics 2021-03-22 Kerstin Beer , Megha Khosla , Julius Köhler , Tobias J. Osborne

Many network analysis and graph learning techniques are based on models of random walks which require to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, it is common to…

Methodology · Statistics 2022-10-28 Vincenzo Perri , Luka V. Petrović , Ingo Scholtes

We present "Diagrams of States", a way to graphically represent and analyze how quantum information is elaborated during the execution of quantum circuits. This introductory tutorial illustrates the basics, providing useful examples of…

Quantum Physics · Physics 2009-04-20 Sara Felloni , Alberto Leporati , Giuliano Strini

This article presents a novel and succinct algorithmic framework via alternating quantum walks, unifying quantum spatial search, state transfer and uniform sampling on a large class of graphs. Using the framework, we can achieve exact…

Quantum Physics · Physics 2025-04-22 Qingwen Wang , Ying Jiang , Lvzhou Li

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models…