Analogue-digital quantum computing

Quantum Technologies and Quantum Machine Learning

Group Description:

The University of Sevilla’s (US) interest in quantum computing lies in contributing to fundamental knowledge in this cutting-edge area, particularly in quantum machine learning, which could have implications not only in the scientific realm but also for society as a whole. Principal investigator Lucas Lamata is one of the leading experts in digital-analog quantum computing and has dedicated recent years of research to its development and application to various problems. Within the framework of Quantum Spain, the US will study the use of this computational paradigm for quantum machine learning and apply it to solving various problems.

Activity description:

Analog-digital quantum computing involves combining elementary operations (quantum gates) used in current quantum computers with periods during which the qubit system evolves according to its natural interactions, perhaps with the mediation of slight external “trained” control. In this activity, the following tasks will be addressed in this context:

  • Analyze how quantum machine learning algorithms, particularly quantum reinforcement learning, can be enhanced with the new paradigm of analog-digital quantum algorithms.
  • Study the implementation of these algorithms on platforms of superconducting circuits, trapped ions, and quantum photonics.


Olivera-Atencio, M. L.; Lamata, L.; Casado-Pascual, J.

Benefits of Open Quantum Systems for Quantum Machine Learning Journal Article

In: Adv Quantum Technologies, 2023, ISBN: 2511-9044.

Abstract | Links | BibTeX | Tags: artificial intelligence, Quantum algorithms, quantum machine learning, US

Martín-Guerrero, J. D.; Lamata, L.; Villmann, T.

Quantum Artificial Intelligence: A tutorial Conference

2023, ISBN: 978-2-87587-088-9.

Abstract | Links | BibTeX | Tags: artificial intelligence, machine learning, US