Enhanced Quantum Sensing through Fundamental Excitations of Correlated Spin Systems

• Grant number: 24/08133-4
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2024
• End date: July 31, 2027
• Field of knowledge: Physical Sciences and Mathematics - Physics - General Physics
• Principal Investigator: Marcos César de Oliveira
• Grantee: Tomás Aguiar Carneiro Ferreira
• Host Institution: Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Quantum sensing is a promising application of quantum technologies that aims to use quantum resources to improve measurement sensitivity. For this goal to be possible, it is necessary to engineer controllable interactions between broad classes of quantum systems. So far, many photonic and hybrid systems have been proposed. However, most quantum platforms are restricted to specific experimental situations, which narrows their potential applicability in technological implementation. In this context, systems that show great promise for broad applicability are spin-correlated systems. That is, systems in which it is possible to generate and control the quantized modes of magnetic lattices: the magnons. As information carriers, a great advantage of magnons is that they possess strong, robust couplings to various physical systems, which allows for efficient control of their generation, propagation and detection, among other characteristics. These qualities make the magnon an ideal choice to be integrated with the known quantum platforms, e.g., cavity photons and superconducting (SC) qubits. Notwithstanding the challenges related to the physical aspects of the problem, the optimal usage of quantum sensors also requires regular characterization and calibration. In general, such calibration is an extremely complex and resource-intensive task - especially when considering systems for estimating multiple parameters, due to the sheer volume of required measurements as well as the computational time needed to analyze those measurements. Machine-learning algorithms present a powerful tool to address this complexity. The discovery of suitable protocols for algorithm usage is, then, vital for the development of sensors for precise quantum-enhanced measurements.In our project, we aim to develop new approaches to the quantum sensing through spin-correlated systems, and to develop a resource optimization for metrology and quantum sensing, through machine learning algorithms.