Quantum Markov chain algorithms


Group description:

The Complutense University of Madrid (UCM) has two research groups specializing in quantum computing, QICC and MATHQI, and is interested in collaborating with other project partners for the development, expansion, and dissemination of this technology. In order to advance research in quantum computing within the fields of its scientific interest, UCM will undertake the activities described in the following section, within the framework of the Quantum Spain project.

The QICC and MATHQI groups, led respectively by professors Miguel Ángel Martín-Delgado and David Pérez-García, have extensive experience in the mathematical analysis of concepts related to quantum computing, as well as in the simulation of quantum systems with tensor networks. The research tasks of these UCM groups will focus on the study of tensor networks and the use of quantum Markov chains in processes related to artificial intelligence.

Activity description:

Quantum Markov chain algorithms are stochastic methods for reproducing probability distributions, which show convergence advantages over classical equivalents. These chains have proven their utility in optimizing a complex problem: protein folding. The main objective of this activity is to develop new quantum optimization, sampling, and machine learning algorithms based on quantum Markov chains.