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Entangled quantum networks provide great flexibilities and scalabilities for quantum information processing or quantum Internet. Most of results are focused on the nonlocalities of quantum networks. Our goal in this work is to explore new…

Quantum Physics · Physics 2021-08-06 Ming-Xing Luo

Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools---called tensor network methods---form the backbone of modern numerical methods…

Quantum Physics · Physics 2021-04-08 Andrey Kardashin , Alexey Uvarov , Jacob Biamonte

We introduce superposition-based quantum networks composed of (i) the classical perceptron model of multilayered, feedforward neural networks and (ii) the algebraic model of evolving reticular quantum structures as described in quantum…

Neurons and Cognition · Quantitative Biology 2009-11-10 Christopher Altman , Jaroslaw Pykacz , Roman Zapatrin

Equivariance of linear neural network layers is well studied. In this work, we relax the equivariance condition to only be true in a projective sense. We propose a way to construct a projectively equivariant neural network through building…

Computer Vision and Pattern Recognition · Computer Science 2023-12-21 Georg Bökman , Axel Flinth , Fredrik Kahl

Reaction networks are widely used models to describe biochemical processes. Stochastic fluctuations in the counts of biological macromolecules have amplified consequences due to their small population sizes. This makes it necessary to favor…

Probability · Mathematics 2022-02-28 Daniele Cappelletti , Badal Joshi

The study of complex networks has been historically based on simple graph data models representing relationships between individuals. However, often reality cannot be accurately captured by a flat graph model. This has led to the…

Social and Information Networks · Computer Science 2013-03-21 Matteo Magnani , Barbora Micenkova , Luca Rossi

We construct a novel class of stochastic blockmodels using Bayesian nonparametric mixtures. These model allows us to jointly estimate the structure of multiple networks and explicitly compare the community structures underlying them, while…

Methodology · Statistics 2016-06-17 Perla Reyes , Abel Rodriguez

In this paper we introduce a new representation for the multistationarity region of a reaction network, using polynomial superlevel sets. The advantages of using this polynomial superlevel set representation over the already existing…

Molecular Networks · Quantitative Biology 2022-09-29 AmirHosein Sadeghimanesh , Matthew England

We give a criterion of classicality for mixed states in terms of expectation values of a quantum observable. Using group representation theory we identify all cases when the criterion can be computed exactly in terms of the spectrum of a…

Mathematical Physics · Physics 2015-06-03 Michał Oszmaniec , Marek Kuś

The design and synthesis of complex and large mimicked biochemical networks de novo is an unsolved problem in synthetic biology. To address this limitation without resorting to ad hoc computations and experiments, a predictive mathematical…

Biological Physics · Physics 2015-01-21 Eisuke Chikayama , R. Craig Everroad

This paper is concerned with a linear fractional representation approach to the synthesis of linear coherent quantum controllers for a given linear quantum plant. The plant and controller represent open quantum harmonic oscillators and are…

Quantum Physics · Physics 2017-03-23 Arash Kh. Sichani , Ian R. Petersen

The aim of this paper is to introduce a new graphic representation of quantum states by means of a specific application: the analysis of two models of quantum copying machines. The graphic representation by diagrams of states offers a clear…

Quantum Physics · Physics 2007-05-23 Sara Felloni , Giuliano Strini

Quantum networks offer a unifying set of opportunities and challenges across exciting intellectual and technical frontiers, including for quantum computation, communication, and metrology. The realization of quantum networks composed of…

Quantum Physics · Physics 2009-11-13 H. J. Kimble

The aim of this paper is to extend linear quantum dynamical network theory to include static Bogoliubov components (such as squeezers). Within this integrated quantum network theory we provide general methods for cascade or series…

Quantum Physics · Physics 2010-02-04 J. E. Gough , M. R. James , H. I. Nurdin

Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes,…

Optimization and Control · Mathematics 2017-06-20 Chrysanthos E. Gounaris , Karthikeyan Rajendran , Ioannis G. Kevrekidis , Christodoulos A. Floudas

The purpose of this paper is to formulate and solve a H-infinity controller synthesis problem for a class of non-commutative linear stochastic systems which includes many examples of interest in quantum technology. The paper includes…

Quantum Physics · Physics 2009-04-09 M. R. James , H. I. Nurdin , I. R. Petersen

A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems is…

The purpose of this paper is to study the realization theory of quantum linear systems. It is shown that for a general quantum linear system its controllability and observability are equivalent and they can be checked by means of a simple…

Quantum Physics · Physics 2014-10-31 John E. Gough , Guofeng Zhang

For the networks that are linear conjugate to complex balanced systems, the delayed version may include two classes of networks: one class is still linear conjugate to the delayed complex balanced network, the other is not. In this paper,…

Dynamical Systems · Mathematics 2022-04-07 Xiaoyu Zhang , Chuanhou Gao , Denis Dochain

Tensor networks are efficient representations of high-dimensional tensors which have been very successful for physics and mathematics applications. We demonstrate how algorithms for optimizing such networks can be adapted to supervised…

Machine Learning · Statistics 2017-05-22 E. Miles Stoudenmire , David J. Schwab