Upcoming Seminars

Juan Torres Luna

TU Delft – Quantum Tinkerer group

 

Understanding the fate of the poor man’s Majorana in the long-chain limit

When: 12:00-13:00 CET, February 18th (Tuesday), 2025

Where: Sala de Seminarios (182), ICMM-CSIC, Campus de Cantoblanco, Madrid

The quality of topological qubits based on Majorana bound states (MBS) depends uniquely on two properties: the size of the topological gap and the MBS localization length. While the Kitaev model features perfectly localized MBS and a maximal gap, realistic systems exhibit finite localization length and the gap is limited by proximity effect, which raises the question of what platform can realise the best MBS. In this work, we study the quality of MBS in two platforms: the Lutchyn-Oreg (LO) model and the quantum dot chain model.
For the quantum dot chain, we find a trade-off between weak coupling—where the gap is small and the localization length is small—and the strong coupling regime—where the gap is large and the localization length is large. For the LO model, we find that the interplay between spin-orbit coupling and momentum yields a gap and localization length that cannot be simultaneously optimized. In order to find the best MBS that each model can achieve, we use multi-objective optimization to find the set of optimal localization lengths and gaps. We demonstrate that quantum dot chains can achieve MBS quality comparable to nanowire models, with extra tunability of the gap and localization. Our results highlight quantum dot chains as a versatile platform for high-quality MBS and topological quantum computing.

 

Amir Rahmani

Instytut Fizyki PAN, Warsaw, Poland

 

Quantum Many-Body Interactions and Light-Matter Coupling: Expanding the Frontier of Reservoir Computing and Machine Learning

When: 12:00-13:00 CET, December 2nd (Tuesday), 2024

Where: Sala de Juntas, ICMM-CSIC, Campus de Cantoblanco, Madrid

Quantum reservoir computing (QRC) has emerged as a powerful paradigm for tackling machine-learning tasks by using the rich dynamics of quantum systems. At its core, QRC relies on effective control drives to encode input data and nonlinear mappings to transform these inputs into output features. Photonic platforms are likely candidates for QRC. However, they often lack sufficient nonlinearity for optimal performance. To overcome this limitation, a hybrid optoelectronic approach can be used, combining the speed of optical processing with the inherent nonlinearity of electronics. In this seminar, we present a method to enhance nonlinearity by employing wavefunction engineering within the regime of light-matter coupling. We explore various structures, including GaAs and TMD materials in cavities and waveguides. Through some examples, we demonstrate improvements in the accuracy of some quantum tasks, highlighting the potential of QRC in advancing machine learning.