Online Seminar #6: Quantum magnonic devices: a flexible tool for quantu technology

Carlos González-Ballestero (Institute of theoretical physics – U Insbruck) Despite its tremendous success, microwave-based quantum technology has limitations that couldbe solved by a hybrid approach, i. e. by interfacing microwaves with another degree of freedomwith complementary properties. Spin waves in ferromagnets, and their quanta magnons, are idealcandidates as they show tuneable spectrum and nonlinearity, have…

Online Seminar #5: Quantum-inspired solutions for privacy leaks in machine learning

Alejandro Pozas-Kerstjens (ICMAT) Vast amounts of data are routinely processed in machine learning pipelines, every time coveringmore aspects of our interactions with the world. However, the quest for performance is leavingother important aspects, such as privacy, on the side. For example, when the models processingthe data are made public, is the safety of the data…

Online Seminar #4: Quantum nonlinear optics based on 2D Rydberg Atom Array

Daniel Gonçalves (ICFO) We explore the combination of sub-wavelength, two-dimensional atomic arrays andRydberg interactions as a powerful platform to realize strong, coherent interactionsbetween individual photons with high fidelity. In particular, the spatial ordering of theatoms guarantees efficient atom-light interactions without the possibility of scatteringlight into unwanted directions, for example, allowing the array to act as…

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…