Manuel G. Algaba (IQM & UAM) Quantum simulation has been one of the main topics of research in the realm of quantum technologies for years. In this talk, we will use it for modelling the hyperpolarization process on a nanodiamond, which can improve medical imaging. We will introduce the basics of hyperpolarization and the quantum…
Welcome to the Spanish Network on Quantum Information (RITCE)
Quantum information is the new language of Quantum Science, describing and explaining the structure of complex quantum systems, entanglement, and their potential for storing, encoding and processing information.
Quantum mechanical systems can be used to enhance common tasks, such as metrology and sensing, as well as to build new forms of computation & cryptography that cannot be beaten by classical devices.
Quantum simulation is the experimental control of atoms, molecules or even solid state devices, to simulate or solve interesting problems from Condensed Matter or Theoretical Physics.
The Spanish Network on Quantum Information (RITCE), promotes research in all of these fields, helping the establishment of collaborations inside the network and with international groups, and promoting the training of new scientists in these topics.
- Online Seminar #3: Co-Design quantum simulation algorithm of nanoscale NMR
- Online Seminar #2: Squeezed lasing
Carlos Sánchez-Muñoz (IFIMAC-UAM), Invited Speaker The laser, originally described to be as a “solution seeking a problem”, is now a ubiquitous piece of technology and arguably one of the most successful practical applications of quantum mechanics. The key property behind its success is its capability to provide high-intensity light with a narrow linewidth and long…
- Online seminar #1: Quantum Approximate Optimization Algorithm for Bayesian network structure learning
Vicente P. Soloviev (UPM) Bayesian network structure learning is an NP-hard problem that has been faced by anumber of traditional approaches in recent decades. In this work, a specific type ofvariational quantum algorithm, the quantum approximate optimization algorithm, wasused to solve the Bayesian network structure learning problem. Our results showed thatthe quantum approximate optimization algorithm…