MAP-i Thesis Proposal
Title Learning open quantum systems using machine learning
Coordinators
• Ernesto Galv˜ao (University of Minho, INL – International Iberian Nanotechnology Lab- oratory)
• Luis Soares Barbosa (University of Minho, INL – International Iberian Nanotechnology Laboratory)
External Member
• Raffaele Santagati. (Boehringer-Ingelheim)
Research Unit International Iberian Nanotechnology Laboratory, University of Minho.
Description The development of accurate models describing the behaviour of quantum sys- tems is essential to improve our understanding and enable the development of quantum tech- nologies. However, noise and unfavourable resource scaling makes it challenging to synthesise physically meaningful models from experimental observations. Ideas from machine learning have shown some success in generating Hamiltonian descriptions of quantum systems based on experimental data. However, these techniques have been limited to open quantum systems with a small number of interacting particles, restricting their applicability. For open systems with a larger environmental bath, the Lindblad master equation is one of the models that can give more accurate predictions of the systems’ dynamics. The objective of this PhD project is to fill this gap, developing and designing new efficient techniques to obtain analytic models (e.g. Lindblad master equations) from experimental observations for a broad family of complex quantum systems. These techniques will likely find applications in optimal control methods for quantum sensors, in quantum error correction and quantum simulation.
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