Application

Interdisciplinary Thematic Platform on Quantum Technologies at CSIC

Group Description:

Within CSIC, the objectives of this project align with one of the fundamental pillars of the Interdisciplinary Thematic Platform on Quantum Technologies, to which the participating research groups belong. This platform coordinates CSIC researchers’ access to quantum computing resources and conducts research on quantum algorithms of both fundamental (scientific) and commercial interest (in collaboration with companies and third parties). CSIC contributes unique expertise in the design of quantum optimization and simulation algorithms, quantum artificial intelligence algorithms, and the operation of quantum computers.

Several groups from CSIC institutes are actively involved in quantum algorithm development. The QUINFOG group at the Institute of Fundamental Physics develops quantum optimization algorithms and quantum machine learning. Additionally, new numerical simulation methods based on tensor networks are being researched. The Institute of Theoretical Physics group, led by professors Germán Sierra and Alejandro Bermúdez, focuses on quantum algorithms applied to processes in condensed matter physics and high-energy physics. The group at the Center for Research in Nanomaterials and Nanotechnology, led by Daniel Barredo, experiments with the use of cold atoms to simulate quantum processes, including circuits and algorithms developed by the community. The Institute of Interdisciplinary Physics and Complex Systems researches new models of quantum computing and artificial intelligence for application to complex systems.

The mentioned QTEP thematic platform coordinates various courses in quantum computing, including the CSIC Master’s in Quantum Technologies, as well as dissemination and outreach activities aimed at businesses. Additionally, the platform promotes transfer activities and the creation of spin-offs.

Activity description:

• Development of quantum and quantum-inspired algorithms to solve optimization and machine learning problems of practical relevance.
• Development of error mitigation techniques and variational methods enabling the use of quantum computers under realistic conditions of noise and imperfections, such as in devices based on trapped ions.
• Development of quantum algorithms for the study and simulation of complex quantum systems in condensed matter and particle physics.
• Tensor networks for the simulation, optimization, and training of quantum algorithms.

Results