SEMINARS

Efficient quantum teleportation under noise with hybrid entanglement 

October 24th 2023, aula AP1, DIFC, Ed. 18, Viale delle Scienze

Jyrki Piilo1,*

1. Department of Physics and Astronomy, University of Turku, Finland

* jyrki.piilo@utu.fi

Quantum entanglement and decoherence are the two counterforces of many quantum technologies and protocols. For example, while quantum teleportation is fueled by a pair of maximally entangled resource qubits, it is vulnerable to decoherence.We propose an efficient quantum teleportation protocol in the presence of pure decoherence and without entangled resource qubits prior to the Bell-state measurement. Instead, we employ multipartite hybrid entanglement between the auxiliary qubits and their local environments within the open-quantum-system context. Interestingly, with a hybrid-entangled initial state, it is the decoherence that allows us to achieve high fidelities. We demonstrate our protocol in an all-optical experiment.

Measuring Time Series Reversibility with Machine Learning

October 24th 2023, aula AP1, DIFC, Ed. 18, Viale delle Scienze 

Christian Bongiorno1,*

1. Laboratoire Mathématiques et Informatique pour la Complexité et les Systèmes, CentraleSupélec, Paris, France

* christian.bongiorno@centralesupelec.fr

In statistical physics and time series analysis, "production entropy" is a key concept that describe the temporal irreversibility of dynamic processes. It measures how different a time series is from its time-reversed version. This measure is related to how predictable the series is. Interestingly, this can be linked to the capability of a regressor to tell apart a time series from its time-reversed version. This connection suggests that various machine learning methods, such as neural networks and gradient boosting, can be used to estimate production entropy. The seminar will examine synthetic models like the Browning girator to show the strengths and limits of these methods. Lastly, the seminar will discuss the application of these ideas to financial returns.