Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

CoQHoNet

Financiado

Connecting Quantum Hopfield Networks

Classical Neural Networks (NNs) are architectures successfully employed in Machine Learning (ML) tasks, such as pattern recognition, analysis of big data, and digitalization. Currently, a full development of quantum technologies i... ver más

31/05/2025

CSIC

165K€

Presupuesto del proyecto: 165K€

Líder del proyecto

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 2-3 | 552M€

Ver 2 Participantes

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-03-28

HORIZON-MSCA-2022-PF-01-01: MSCA Postdoctoral Fellowships 2022 ExpectedOutcome:

I+D

Cerrada hace 2 años

0% 100%

2 Participantes

CSIC

165.31K€ | Lider

UIB

N.D. | Participante

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

HyNNet NISQ Hybrid quantum-classical neural networks for the characteriz... 219K€ Cerrado

PID2021-128970OA-I00 MACHINE LEARNING CUANTICO DISIPATIVO: UN PUENTE ENTRE LOS SI... 104K€ Cerrado

qDynnet Quantum dynamical neural networks 1M€ Cerrado

FIS2016-80681-P APRENDIZAJE CUANTICO Y NO CLASICALIDAD 185K€ Cerrado

CORaL Correlations-Oriented Representation Learning 173K€ Cerrado

CNS2023-145392 Ciencias de la computación y tecnología informática 399K€ Cerrado

Duración del proyecto: 26 meses Fecha Inicio: 2023-03-28
Fecha Fin: 2025-05-31

Líder del proyecto

AGENCIA ESTATAL CONSEJO SUPERIOR DE INVESTIGA... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 2-3 | 552M€

Presupuesto del proyecto 165K€

Descripción del proyecto Classical Neural Networks (NNs) are architectures successfully employed in Machine Learning (ML) tasks, such as pattern recognition, analysis of big data, and digitalization. Currently, a full development of quantum technologies is considered the most promising improvement on classical ML. Motivated by this, various contributions are focusing on the emerging field of quantum NNs, regarded as the backbones of quantum ML. Though strategic to tackle digitalization challenges of Europe concerning e.g. secure information, a unifying framework for quantum NNs is still missing. This project aims at contributing to the general effort of defining the main features of quantum NNs, and further exploiting them for practical purposes. More concretely, it considers quantum generalizations of associative memory-type NNs, such as the prototypical example referred to as Hopfield NN. To start with, associative memories can perform relatively easy tasks such as pattern retrieval. However, they are employed in more complex architectures too, as it is the case of Hopfield NNs when embedded in Boltzmann Machines. State-of-the-art quantum associative memories exploit quantum spin NNs and bosonic platforms, describing them as Markovian, or memory-less, open quantum systems. Importantly, these setups are relevant for condensed matter and photonic implementations. Inserting these proposals in a more general theoretical frame can provide insights to answer timely questions, such as storing non-classical states, accounting for non-Markovian effects on the retrieval task, characterizing the storage capacity. The challenge of this proposal consists in unveiling the bridge between Markovian quantum Hopfield-type NNs, and the formalism of quantum maps. Fulfilling such a gap further enables to use associative memories for designing near-term quantum devices that are robust to detrimental effects of the environment, which generically cause deterioration of e.g. coherence and entanglement.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores