Controlling Quantum System Properties through Automatic Differentiation

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This seminar covered the results in Machine Learning Catalysis of quantum tunneling.

Abstract: The so-called second quantum technological revolution is evolving at a rapid pace and promises significant impacts not only on science but also within the industrial sector.  Progress in this field critically relies on efficient methods for controlling the quantum properties of systems and their dynamics. In this talk we are going to give two illustrative examples of how, using a key algorithm in modern machine learning, automatic differentiation, we can control the properties of interest of a quantum system. Our initial case study focuses on the control of the tunnelling probability of particles in a two-mode system. We show that when the quantum system is coupled to an ancilla, one can learn the optimal ancillary component and the optimal coupling, such that the tunnelling probability/time can be controlled. The subsequent example addresses the mitigation of decoherence within a quantum system with noise. Employing a similar methodology, we show how we can learn an ancillary system and its corresponding noise parameters to counteract and diminish the impact of system noise.

by Prof. Fabio Anselmi (Department of Mathematics, Informatics, and Geo-science, University of Trieste)

• Look into Autodiff, Quantum noise, Quantum tunnelling, Activation Layers, ancillary system, Bose-Hubbard Model $H_S={\eta }_S{J}_z^2+{\gamma }_S{J}_X-{\Delta }_S{J}_z$ ( Particle Interaction + Tunnelling + Asymmetry ), Basin of attraction ( Machine learning ), Noon state, Stochastic resonance, Characterisation of positive maps vs Completely Positive maps