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Related papers: Quantum Expander Mixing Lemma and its Structural C…

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We present a simple way to quantize the well-known Margulis expander map. The result is a quantum expander which acts on discrete Wigner functions in the same way the classical Margulis expander acts on probability distributions. The…

Quantum Physics · Physics 2008-05-29 D. Gross , J. Eisert

We prove a generalization of the Expander Mixing Lemma for arbitrary (finite) simplicial complexes. The original lemma states that concentration of the Laplace spectrum of a graph implies combinatorial expansion (which is also referred to…

Combinatorics · Mathematics 2018-04-11 Ori Parzanchevski

We formulate and prove inverse mixing lemmas in the settings of simplicial complexes and k-uniform hypergraphs. In the hypergraph setting, we extend results of Bilu and Linial for graphs. In the simplicial complex setting, our results…

Combinatorics · Mathematics 2014-07-10 Emma Cohen , Dhruv Mubayi , Peter Ralli , Prasad Tetali

Recent progress in applying complex network theory to problems in quantum information has resulted in a beneficial crossover. Complex network methods have successfully been applied to transport and entanglement models while information…

Quantum Physics · Physics 2019-05-22 Jacob Biamonte , Mauro Faccin , Manlio De Domenico

A conjecture regarding the structure of expander graphs is discussed.

Combinatorics · Mathematics 2020-10-20 Itai Benjamini , Mikolaj Fraczyk

Quantum networks are important for quantum communication, enabling tasks such as quantum teleportation, quantum key distribution, quantum sensing, and quantum error correction, often utilizing graph states, a specific class of multipartite…

Quantum Physics · Physics 2025-11-19 Aniruddha Sen , Kenneth Goodenough , Don Towsley

In this work we show that given a connectivity graph $G$ of a $[[n,k,d]]$ quantum code, there exists $\{K_i\}_i, K_i \subset G$, such that $\sum_i |K_i|\in \Omega(k), \ |K_i| \in \Omega(d)$, and the $K_i$'s are $\tilde{\Omega}(…

Information Theory · Computer Science 2023-09-29 Nouédyn Baspin

The connection between certain entangled states and graphs has been heavily studied in the context of measurement-based quantum computation as a tool for understanding entanglement. Here we show that this correspondence can be harnessed in…

Quantum Physics · Physics 2016-03-23 Liming Zhao , Carlos A. Pérez-Delgado , Joseph F. Fitzsimons

We define a class of quantum systems called regular quantum graphs. Although their dynamics is chaotic in the classical limit with positive topological entropy, the spectrum of regular quantum graphs is explicitly computable analytically…

Quantum Physics · Physics 2007-05-23 R. Blümel , Yu. Dabaghian , R. V. Jensen

Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the appearance of Graph Transformers using global graph…

Quantum Physics · Physics 2022-10-20 Slimane Thabet , Romain Fouilland , Loic Henriet

A fundamental fact about bounded-degree graph expanders is that three notions of expansion -- vertex expansion, edge expansion, and spectral expansion -- are all equivalent. In this paper, we study to what extent such a statement is true…

Combinatorics · Mathematics 2023-01-16 Yinan Li , Youming Qiao , Avi Wigderson , Yuval Wigderson , Chuanqi Zhang

We study the statistical properties of the scattering matrix associated with generic quantum graphs. The scattering matrix is the quantum analogue of the classical evolution operator on the graph. For the energy-averaged spectral form…

Chaotic Dynamics · Physics 2009-10-31 Tsampikos Kottos , Holger Schanz

Coboundary and cosystolic expansion are notions of expansion that generalize the Cheeger constant or edge expansion of a graph to higher dimensions. The classical Cheeger inequality implies that for graphs edge expansion is equivalent to…

Combinatorics · Mathematics 2021-02-11 Tali Kaufman , Izhar Oppenheim

Deploying graph neural networks (GNNs) on whole-graph classification or regression tasks is known to be challenging: it often requires computing node features that are mindful of both local interactions in their neighbourhood and the global…

Machine Learning · Computer Science 2022-12-22 Andreea Deac , Marc Lackenby , Petar Veličković

Graph kernels are often used in bioinformatics and network applications to measure the similarity between graphs; therefore, they may be used to construct efficient graph classifiers. Many graph kernels have been developed thus far, but to…

Quantum Physics · Physics 2022-11-01 Kaito Kishi , Takahiko Satoh , Rudy Raymond , Naoki Yamamoto , Yasubumi Sakakibara

Graph neural networks operate on graph-structured data via exchanging messages along edges. One limitation of this message passing paradigm is the over-squashing problem. Over-squashing occurs when messages from a node's expanded receptive…

Machine Learning · Computer Science 2023-11-15 Thomas Christie , Yu He

Quantum networks are essential to quantum information distributed applications, and communicating over them is a key challenge. Complex networks have rich and intriguing properties, which are as yet unexplored in the quantum setting. Here,…

Quantum Physics · Physics 2009-12-15 M. Cuquet , J. Calsamiglia

This work studies how a suitably-designed classical system generates with a quantum-like (QL) state space mediated by a graph. The graph plays a special dual role by directing the topology of the classical network and defining a state space…

Quantum Physics · Physics 2026-03-24 Gregory D. Scholes

The paper deals with some spectral properties of (mostly infinite) quantum and combinatorial graphs. Quantum graphs have been intensively studied lately due to their numerous applications to mesoscopic physics, nanotechnology, optics, and…

Mathematical Physics · Physics 2009-11-10 Peter Kuchment

In an increasingly interconnected world, understanding and summarizing the structure of these networks becomes increasingly relevant. However, this task is nontrivial; proposed summary statistics are as diverse as the networks they…

Statistics Theory · Mathematics 2020-04-15 Lee M. Gunderson , Gecia Bravo-Hermsdorff