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NexGenTeN

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State of the art simulations of quantum many body systems with the next generati...

State of the art simulations of quantum many body systems with the next generation tensor network algorithms One of the biggest and most relevant challenges in physics is the accurate study of strongly correlated quantum many-body systems, which give rise to very remarkable phenomena like high-Tc superconductivity (HTSC), quantum spin li... ver más

30/11/2026

UvA

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITEIT VAN AMSTERDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2020-12-17

ERC-2020-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 4 años

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2 Participantes

UvA

2.00M€ | Lider

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Duración del proyecto: 71 meses Fecha Inicio: 2020-12-17
Fecha Fin: 2026-11-30

Líder del proyecto

UNIVERSITEIT VAN AMSTERDAM No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto One of the biggest and most relevant challenges in physics is the accurate study of strongly correlated quantum many-body systems, which give rise to very remarkable phenomena like high-Tc superconductivity (HTSC), quantum spin liquids with topological order, and other novel phases of matter. Understanding these systems is the key to designing new materials and quantum devices for future groundbreaking technologies. In recent years enormous progress in the study of these systems has been achieved with two-dimensional tensor network algorithms, which can be seen as a generalization of the powerful density-matrix renormalization group method to higher dimensions. While already the current algorithms are very powerful and outperform other state-of-the-art approaches, the development of new algorithms with a higher accuracy and broader application range is crucial to enable to solve the most pressing open problems. Building upon my previous breakthroughs in this field, I will develop the next generation of tensor network algorithms, including novel powerful methods for ground states, time evolution, spectral functions, finite temperature, open systems, and multi-scale approaches. I will use them for groundbreaking simulations of relevant open problems in several fields with unprecedented accuracy. Major milestones include (1) simulations of realistic models of cuprate materials in order to shed new light on the pseudogap phase and pairing mechanism in HTSC, (2) cutting-edge simulations of frustrated materials to reveal the nature of excitations and thermodynamic properties of quantum spin liquids and other challenging states, and (3) predictions of novel states of matter in SU(N) and open quantum systems which can be realized in experimental quantum simulators. This ambitious project will strongly advance our fundamental understanding of strongly correlated systems and set a new state-of-the-art in simulating quantum many-body problems.

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