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QTEngine

Financiado

Quantum Tensor Engine  

Quantum computers harness fundamental aspects of quantum behavior to drive exponential increases in the speed with which certain computations can be performed. They have potentially a tremendous long-term impact in areas such as q... ver más

31/12/2025

TUM

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-02-12

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2023-POC: ERC PROOF OF CONCEPT GRANTS

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TUM

150.00K€ | Lider

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Últimas noticias

30-11-2024: Cataluña Gestión For... Se ha cerrado la línea de ayuda pública: Gestión Forestal Sostenible para Inversiones Forestales Productivas para el organismo:

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

Duración del proyecto: 22 meses Fecha Inicio: 2024-02-12
Fecha Fin: 2025-12-31

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto Quantum computers harness fundamental aspects of quantum behavior to drive exponential increases in the speed with which certain computations can be performed. They have potentially a tremendous long-term impact in areas such as quantum-many body physics and material science, and further afield in machine learning. The quantum many-body problems studied by condensed matter physicists are perhaps the most likely to yield early demonstrations of this potential. However, current and near-term intermediate-scale quantum (NISQ) devices are limited in the number of operations that they can carry out before their performance is degraded by interactions with the environment. To take advantage of these platforms and to outperform classical computers, highly efficient and specialized quantum algorithms are required. The implementation and benchmarking of these basic algorithms on different quantum computing platforms is challenging and requires a detailed knowledge of the underlying physics. Our approach is to produce a ready-to-use, highly innovative software package based upon quantum tensor networks. The Quantum Tensor Engine (QTEngine) will provide a unifying framework for both quantum and classical algorithms. The QTEngine will serve as an engine to drive fast and easy implementation of quantum simulation, quantum machine learning, and optimization algorithms. The anticipated user base include academic groups as well as commercial research and development groups.

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