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Concept of a family of local atoms in n-Hilbert space is being studied. K-frame in tensor product of n-Hilbert spaces is described and a characterization is given. Atomic system in tensor product of n-Hilbert spaces is presented and…

Functional Analysis · Mathematics 2023-03-29 Prasenjit Ghosh , T. K. Samanta

The computational abilities of theories within the generalised probabilistic theory framework has been the subject of much recent study. Such investigations aim to gain an understanding of the possible connections between physical…

Quantum Physics · Physics 2021-08-27 Ciarán M. Gilligan-Lee

We start by an introduction to the basic concepts of computability theory and the introduction of the concept of Turing machine and computation universality. Then se turn to the exploration of trade-offs between different measures of…

Computational Complexity · Computer Science 2011-04-19 Joost J. Joosten , Fernando Soler-Toscano , Hector Zenil

A large class of quantum phase transitions for quantum lattice systems are characterized by local order parameters. It is shown that local order parameters may be systematically constructed from tensor network representations of quantum…

Statistical Mechanics · Physics 2008-03-06 Huan-Qiang Zhou

Although quantum systems are generally described by quantum state vectors, we show that in certain cases their measurement processes can be reformulated as probabilistic equations expressed in terms of probabilistic state vectors. These…

Neurons and Cognition · Quantitative Biology 2025-10-24 Volker Tresp , Hang Li , Federico Harjes , Yunpu Ma

This Master's thesis has two central subjects: the simulation of correlations generated by local measurements on entangled quantum states by local hidden-variables models and the revelation of hidden nonlocality. We present and detail the…

Quantum Physics · Physics 2014-07-23 Leonardo Guerini

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in 'big data'. A crossover between quantum…

We show how to learn structures of generic, non-Markovian, quantum stochastic processes using a tensor network based machine learning algorithm. We do this by representing the process as a matrix product operator (MPO) and train it with a…

Quantum Physics · Physics 2021-01-04 Chu Guo , Kavan Modi , Dario Poletti

Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum…

Quantum Physics · Physics 2023-02-06 Diego Tancara , Hossein T. Dinani , Ariel Norambuena , Felipe F. Fanchini , Raúl Coto

The relation between entanglement entropy and the computational difficulty of classically simulating Quantum Mechanics is briefly reviewed. Matrix product states are proven to provide an efficient representation of one-dimensional quantum…

Quantum Physics · Physics 2008-11-26 Jose I. Latorre

We introduce a certain type of representations for the quantum Teichmuller space of a punctured surface, which we call local representations. We show that, up to finitely many choices, these purely algebraic representations are classified…

Geometric Topology · Mathematics 2007-07-17 Hua Bai , Francis Bonahon , Xiaobo Liu

Belief propagation has recently emerged as a powerful framework for evaluating tensor networks in higher dimensions, combining computational efficiency with provable analytical guarantees. In this work, we develop the first end-to-end…

Quantum Physics · Physics 2026-04-24 Siddhant Midha , Yifan F. Zhang , Daniel Malz , Dmitry A. Abanin , Sarang Gopalakrishnan

Quantum computations usually take place under the control of the classical world. We introduce a Classically-controlled Quantum Turing Machine (CQTM) which is a Turing Machine (TM) with a quantum tape for acting on quantum data, and a…

Quantum Physics · Physics 2016-10-11 Simon Perdrix , Philippe Jorrand

Two gapped quantum ground states in the same phase are connected by an adiabatic evolution which gives rise to a local unitary transformation that maps between the states. On the other hand, gapped ground states remain within the same phase…

Strongly Correlated Electrons · Physics 2015-05-18 Xie Chen , Zheng-Cheng Gu , Xiao-Gang Wen

This paper introduces a formalism that aims to describe the intricacies of quantum computation by establishing a connection with the mathematical foundations of tensor theory and multilinear maps. The focus is on providing a comprehensive…

Quantum Physics · Physics 2024-09-17 Valentina Amitrano , Francesco Pederiva

Quantum measurement is universal for quantum computation. This universality allows alternative schemes to the traditional three-step organisation of quantum computation: initial state preparation, unitary transformation, measurement. In…

Quantum Physics · Physics 2007-05-23 Simon Perdrix , Philippe Jorrand

The study of local models using finite shared randomness originates from the consideration about the cost of classically simulating entanglement in composite quantum systems. We construct explicitly two families of local-hidden-state (LHS)…

Quantum Physics · Physics 2019-09-04 Yuan-Yuan Zhang , Fu-Lin Zhang

We investigate the effect of phase randomness in Ising-type quantum networks. These networks model a large class of physical systems. They describe micro- and nanostructures or arrays of optical elements such as beam splitters…

Quantum Physics · Physics 2015-06-26 P. Torma , I. Jex , W. P. Schleich

Validation of a presumably universal theory, such as quantum mechanics, requires a quantum mechanical description of systems that carry out theoretical calculations and experiments. The description of quantum computers is under active…

Quantum Physics · Physics 2008-02-03 Paul Benioff

Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations mitigate the exponential complexity of many-body systems by capturing only the most…

Machine Learning · Computer Science 2026-04-17 Guillermo Valverde , Igor García-Olaizola , Giannicola Scarpa , Alejandro Pozas-Kerstjens
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