{"id":168340,"date":"2025-01-09T16:12:13","date_gmt":"2025-01-09T15:12:13","guid":{"rendered":"https:\/\/eurecat.org\/?post_type=avada_portfolio&p=168340"},"modified":"2025-01-09T16:12:45","modified_gmt":"2025-01-09T15:12:45","slug":"qia","status":"publish","type":"avada_portfolio","link":"https:\/\/eurecat.org\/es\/portfolio-items\/qia\/","title":{"rendered":"QIA"},"content":{"rendered":"

\"QIA<\/p>\n<\/div>

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Potenciando la computaci\u00f3n cu\u00e1ntica con inteligencia artificial (IA).<\/strong><\/p>\n

QIA<\/strong> desarrolla herramientas basadas en IA orientadas a la optimizaci\u00f3n de algoritmos cu\u00e1nticos<\/strong> y a abordar las limitaciones de hardware asociadas a la computaci\u00f3n cu\u00e1ntica.<\/p>\n

Concretamente, el proyecto optimiza recursos computacionales por algoritmos basados \u200b\u200ben puertas y redes de tensores<\/strong>, con la ayuda de la IA para mejorar la ejecuci\u00f3n de estos algoritmos a trav\u00e9s de las m\u00e9tricas y reducir operaciones necesarias para su implementaci\u00f3n.<\/p>\n

Tambi\u00e9n se desarrollan herramientas basadas en IA para el dise\u00f1o, control y mitigaci\u00f3n de errores<\/strong> en dispositivos cu\u00e1nticos basados \u200b\u200ben tecnolog\u00eda superconductora, especialmente en flujos qubits.<\/p>\n

Por \u00faltimo, se dedica una parte importante del proyecto a la investigaci\u00f3n de algoritmos de IA cu\u00e1ntica, con especial atenci\u00f3n a las Quantum Boltzmann Machines por su adaptabilidad a hardware anal\u00f3gico.<\/p>\n

QIA est\u00e1 liderado por la empresa Qilimanjaro<\/a>, con la participaci\u00f3n del Barcelona Supercomputing Center<\/a> y Eurecat. Por parte del centro tecnol\u00f3gico, la Unidad de Tecnolog\u00edas Audiovisuales<\/a> desarrolla algoritmos de quantum machine learning<\/em> aplicados a problemas de visi\u00f3n como la clasificaci\u00f3n multiclase de im\u00e1genes, mientras que la Unidad de Applied Artificial Intelligence<\/a> crea de m\u00e9todos de reinforcement learning<\/em> para optimizar circuitos cu\u00e1nticos y calibrar chips cu\u00e1nticos.<\/p>\n<\/div>

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Datos generales<\/h3><\/span>
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Proyecto<\/strong><\/p>\n

QIA \u2013 Potenciando la computaci\u00f3n cu\u00e1ntica con inteligencia artificial<\/p>\n<\/div>

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Referencia del proyecto<\/strong><\/p>\n

CPP2023-010459<\/p>\n<\/div>

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Programa y convocatoria<\/strong><\/p>\n

Proyecto financiado por el Ministerio de Ciencia, Innovaci\u00f3n y Universidades a trav\u00e9s de la convocatoria de proyectos de colaboraci\u00f3n p\u00fablico-privada del Plan Estatal de Investigaci\u00f3n Cient\u00edfica, T\u00e9cnica y de Innovaci\u00f3n 2021-2023<\/p>\n<\/div>

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ODS relacionados<\/strong><\/p>\n

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<\/i><\/i><\/span>9. Industria, innovaci\u00f3n e infraestructura<\/span><\/a><\/h4><\/div>
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Desarrollo de herramientas basadas en IA y optimizaci\u00f3n de algoritmos cu\u00e1nticos, avanzando en tecnolog\u00edas punteras como la computaci\u00f3n cu\u00e1ntica y la IA. Esto fomenta la innovaci\u00f3n y posiciona a las industrias participantes como l\u00edderes en tecnolog\u00edas emergentes.<\/p>\n<\/div><\/div><\/div><\/div><\/div>

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