The University of Granada is highly active in the fields of quantum computing and artificial intelligence. Evidence of this is the group formed by professors Jara Juana Bermejo Vega and Daniel Manzano, both researchers with extensive experience in quantum technologies. In particular, researcher Bermejo Vega has participated in numerous studies on the computational complexity of quantum algorithms and their potential quantum advantage over classical ones (as an example, this highly cited article Physical Review X 8, 021010, 2018). The computational advantage in quantum machine learning is an area to explore in the field, and their expertise will be crucial for studying this advantage.
In this activity, we will address lines of work at the intersection of quantum computing and artificial intelligence, with the following specific subtasks:
- Develop quantum algorithms based on complex neural networks, adapting models inspired by the behavior of biological neural networks for execution on current quantum computers. Also, study the application and usefulness of these computational models.
- Analyze the utility of machine learning with neural networks in processing quantum information, specifically in quantum benchmarking problems such as quantum state tomography and verification of circuits executed on quantum computers.
- Use quantum artificial intelligence algorithms, such as variational methods, to optimize the preparation of topological states with utility in quantum error correction. The costs of the studied protocols will be analyzed, with emphasis on quantum resources: the number of qubits, logical gates, and the depth of quantum circuits.