Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlowTM to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multimodal fiber. We show that trainable operators at the input and the readout enable to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.
Our project within the Quantum Technologies Flagship has been launched. The topic is quantum simulations of fundamental physical processes, as Bose-Einstein condensation of photons and gravitational physics!
The project involves Sorbonne University, CNRS, Sapienza, UBO, IST, Imperial, CNR and UPDiderot
We explore the nonlinear response of plasmonic materials driven by ultrashort pulses of electromagnetic radiation with temporal duration of few femtoseconds and high peak intensity. By developing the Fokker-Planck-Landau theory of electron collisions, we solve analytically the collisional integral and derive a novel set of hydrodynamical equations accounting for plasma dynamics at ultrashort time scales. While in the limit of small light intensities we recover the well established Drude model of plasmas, in the high intensity limit we observe nonlinear quenching of collision-induced damping leading to absorption saturation. Our results provide a general background to understand electron dynamics in plasmonic materials with promising photonic applications in the manipulation of plasma waves with reduced absorption at the femtosecond time scale.
Andrea Marini, Alessandro Ciattoni, and Claudio Conti, Collision quenching in the ultrafast dynamics of plasmonic materials in ArXiv:1808.03669