Innovating Works

HyNNet NISQ

Financiado
Hybrid quantum-classical neural networks for the characterization of noisy inter...
Hybrid quantum-classical neural networks for the characterization of noisy intermediate scale quantum computers The objective of HyNNet NISQ is to develop tools based on hybrid quantum-classical algorithms for the characterization and measurement of quantum states prepared on near-term quantum computers. Currently available quantum devices... The objective of HyNNet NISQ is to develop tools based on hybrid quantum-classical algorithms for the characterization and measurement of quantum states prepared on near-term quantum computers. Currently available quantum devices can perform computations that are challenging for classical computers. However, applications of quantum computers in science and economy require a further development of quantum hardware and algorithms. One of the major challenges is the measurement and characterization of quantum states produced as an output of quantum algorithms. Standard diagnostic techniques have become limited due to the quickly increasing system size and complexity of quantum devices. Here I will integrate adaptive quantum algorithms with classical artificial neutral networks to design hybrid quantum-classical neural networks. Employing machine learning techniques, I will train the hybrid neural networks to identify underlying characteristics of quantum states. I will develop characterization and measurement tools required for the simulation of condensed matter physics and quantum chemistry on near-term quantum computers. First, I will investigate how to design and train hybrid neural networks to recognize quantum phases of matter, focusing on strongly correlated systems and topological order. Second, I will study how to exploit hybrid neural networks to reconstruct the full quantum state describing all properties of a quantum system. I will use this technique to efficiently measure quantities required for condensed matter physics and quantum chemistry simulations. The hybrid neural networks developed here can be readily realized on near-term quantum computers. Therefore, they will provide key tools for the development of quantum algorithms and next-generation quantum hardware. I (Dr. Petr Zapletal) will carry out the proposed research with the input and advice from Prof. Christoph Bruder (University of Basel) and Prof. Michael J. Hartmann (FAU Erlangen-Nuremberg). ver más
30/11/2025
FAU
219K€
Duración del proyecto: 31 meses Fecha Inicio: 2023-04-13
Fecha Fin: 2025-11-30

Línea de financiación: concedida

El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-13
Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:
HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022
Cerrada hace 2 años
Presupuesto El presupuesto total del proyecto asciende a 219K€
Líder del proyecto
FRIEDRICHALEXANDERUNIVERSITAET ERLANGENNUERNB... No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico TRL 4-5