Formation of oriented polar ultracold molecules: an investigation through quantum optimal control and machine learning.

• Grant number: 24/09015-5
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: October 01, 2024
• End date: September 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Physics - Atomic and Molecular Physics
• Principal Investigator: Edson Denis Leonel
• Grantee: Murilo Deliberali Forlevesi
• Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil

Abstract

The production of ultracold polar molecules is an important undertaking for Physics due to its applications ranging from quantum computing to metrology. As direct cooling of molecules is still a technological challenge, an alternative is the creation of molecules from pre-cooled atoms using external electromagnetic fields. The alignment and orientation of molecules is another relevant objective that can be seen as a crucial step towards controlling more complex scenarios. For example, increasing the product of a chemical reaction often requires controlling the spatial orientation of the molecules. The present proposal focuses on investigating the formation of ultracold oriented polar molecules through external fields. Combining the purposes of training and guidance, the proposal aims to understand the fields' ability to control this double objective, opening the possibility for new experiments. Recent research has shown a certain degree of alignment of molecules formed by photoassociation, so it is expected that the purpose of the project can be achieved. To obtain the control fields, two different methodologies will be used. The first is based on quantum optimal control, which is a consolidated theory in molecular physics and crucial for the development of quantum technologies. The second is based on reinforcement learning, which is a category of algorithm that falls within the context of machine learning in artificial intelligence. Due to its ability to solve complex problems, reinforcement learning techniques have been applied to a wide variety of problems in quantum mechanics, including the preparation of quantum gates. Therefore, the project will also seek to clarify whether reinforcement learning techniques suggest new control strategies and how they compare with quantum optimal control in a problem involving scattering states.