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  1. Article ; Online: Efficient Tensor Network Ansatz for High-Dimensional Quantum Many-Body Problems.

    Felser, Timo / Notarnicola, Simone / Montangero, Simone

    Physical review letters

    2021  Volume 126, Issue 17, Page(s) 170603

    Abstract: We introduce a novel tensor network structure augmenting the well-established tree tensor network representation of a quantum many-body wave function. The new structure satisfies the area law in high dimensions remaining efficiently manipulatable and ... ...

    Abstract We introduce a novel tensor network structure augmenting the well-established tree tensor network representation of a quantum many-body wave function. The new structure satisfies the area law in high dimensions remaining efficiently manipulatable and scalable. We benchmark this novel approach against paradigmatic two-dimensional spin models demonstrating unprecedented precision and system sizes. Finally, we compute the ground state phase diagram of two-dimensional lattice Rydberg atoms in optical tweezers observing nontrivial phases and quantum phase transitions, providing realistic benchmarks for current and future two-dimensional quantum simulations.
    Language English
    Publishing date 2021-05-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.126.170603
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  2. Article ; Online: Lattice quantum electrodynamics in (3+1)-dimensions at finite density with tensor networks.

    Magnifico, Giuseppe / Felser, Timo / Silvi, Pietro / Montangero, Simone

    Nature communications

    2021  Volume 12, Issue 1, Page(s) 3600

    Abstract: Gauge theories are of paramount importance in our understanding of fundamental constituents of matter and their interactions. However, the complete characterization of their phase diagrams and the full understanding of non-perturbative effects are still ... ...

    Abstract Gauge theories are of paramount importance in our understanding of fundamental constituents of matter and their interactions. However, the complete characterization of their phase diagrams and the full understanding of non-perturbative effects are still debated, especially at finite charge density, mostly due to the sign-problem affecting Monte Carlo numerical simulations. Here, we report the Tensor Network simulation of a three dimensional lattice gauge theory in the Hamiltonian formulation including dynamical matter: Using this sign-problem-free method, we simulate the ground states of a compact Quantum Electrodynamics at zero and finite charge densities, and address fundamental questions such as the characterization of collective phases of the model, the presence of a confining phase at large gauge coupling, and the study of charge-screening effects.
    Language English
    Publishing date 2021-06-14
    Publishing country England
    Document type Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-021-23646-3
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  3. Article ; Online: Optimizing radiotherapy plans for cancer treatment with Tensor Networks.

    Cavinato, Samuele / Felser, Timo / Fusella, Marco / Paiusco, Marta / Montangero, Simone

    Physics in medicine and biology

    2021  Volume 66, Issue 12

    Abstract: We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the intensity-modulated radiation therapy technique-that allows treating irregular and ...

    Abstract We present a novel application of Tensor Network methods in cancer treatment as a potential tool to solve the dose optimization problem in radiotherapy. In particular, the intensity-modulated radiation therapy technique-that allows treating irregular and inhomogeneous tumors while reducing the radiation toxicity on healthy organs-is based on the optimization problem of the beamlets intensities that shall result in a maximal delivery of the therapy dose to cancer while avoiding the organs at risk of being damaged by the radiation. The resulting optimization problem is expressed as a cost function to be optimized. Here, we map the cost function into an Ising-like Hamiltonian, describing a system of long-range interacting qubits. Finally, we solve the dose optimization problem by finding the ground-state of the Hamiltonian using a Tree Tensor Network algorithm. In particular, we present an anatomical scenario exemplifying a prostate cancer treatment. A similar approach can be applied to future hybrid classical-quantum algorithms, paving the way for the use of quantum technologies in future medical treatments.
    MeSH term(s) Algorithms ; Humans ; Male ; Prostatic Neoplasms/radiotherapy ; Radiation Injuries ; Radiotherapy Planning, Computer-Assisted ; Radiotherapy, Intensity-Modulated
    Language English
    Publishing date 2021-06-16
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ac01f2
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  4. Book ; Online: Quantum-inspired Machine Learning on high-energy physics data

    Felser, Timo / Trenti, Marco / Sestini, Lorenzo / Gianelle, Alessio / Zuliani, Davide / Lucchesi, Donatella / Montangero, Simone

    2020  

    Abstract: Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a ... ...

    Abstract Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning technique to a very important and challenging big data problem in high energy physics: the analysis and classification of data produced by the Large Hadron Collider at CERN. In particular, we present how to effectively classify so-called b-jets, jets originating from b-quarks from proton-proton collisions in the LHCb experiment, and how to interpret the classification results. We exploit the Tensor Network approach to select important features and adapt the network geometry based on information acquired in the learning process. Finally, we show how to adapt the tree tensor network to achieve optimal precision or fast response in time without the need of repeating the learning process. These results pave the way to the implementation of high-frequency real-time applications, a key ingredient needed among others for current and future LHCb event classification able to trigger events at the tens of MHz scale.

    Comment: 13 pages, 4 figures
    Keywords Statistics - Machine Learning ; Condensed Matter - Statistical Mechanics ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability ; Quantum Physics
    Subject code 006
    Publishing date 2020-04-28
    Publishing country us
    Document type Book ; Online
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