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.
This activity aims to develop quantum simulation algorithms based on trapped neutral atoms with optical tweezers, a platform with capabilities similar to superconducting technology for solving optimization, simulation, and machine learning problems. The objectives of this activity include:
- Designing control technology for a quantum simulator.
- Developing learning algorithms and feedback based on machine learning to optimize the simulation.
- Creating hybrid quantum algorithms for solving large QUBO problems (~100 variables) on this platform.