Contact persons: Marianne E. Bathen, Lasse Vines
Keywords: Quantum sensing, Digital twins, Materials science, Deep learning
Research group: Semiconductor Physics, CCSE
Department of Physics
Quantum sensors (https://en.wikipedia.org/wiki/Quantum_sensor) exploit the unique properties of quantum systems to detect minute variations in, e.g., electromagnetic fields, pressure, and temperature. This is expected to enhance precision and go beyond the limits of current sensor technology, with drastic impact on fields like biology, materials science and sustainability. The green transition is also dependent on a digital transition, which again cannot be fulfilled without the key component, namely the physical sensor. This is typically referred to as the twin-transition, were the two interlink in order to, e.g., reduce CO2 emissions and energy consumption. We are interested in research on quantum sensors from both a methodological and theoretical perspective, as we believe that a synergy between theoretical, numerical and experimental research is essential for their realization. Research proposals may span several methodological approaches within this scope, as well as various application domains.
Topics from methodological research:
- Theory of quantum sensing: development of theoretical frameworks and models to understand the fundamental limits and capabilities of quantum sensors.
- Development of quantum metrology techniques for high-precision measurements.
- Numerical optimization techniques to design and optimize control sequences that maximize the sensitivity and precision of quantum sensors.
- Data handling/processing for output of quantum sensors.
- Neural networks for data analysis of output from quantum sensors.
- Decision making based on quantum sensors (uncertainty and decoherence).
- Quantum information processing for quantum sensing: Use of quantum information processing techniques, such as quantum machine learning, to enhance the efficiency, reliability, and speed of quantum sensing devices.
- Quantum simulation for quantum sensing: use of numerical simulations, such as quantum Monte Carlo methods or quantum mechanical many-body methods, to study and analyze quantum sensing systems.
Topics from natural sciences or technology:
- Material platforms for quantum sensing.
- Modeling of decoherence in quantum sensors.
- Quantum sensing in complex environments such as space or subsea.
- Quantum sensing in biological systems.
- Use of quantum sensing for smart grids and the green transition.
Application domains:
- Nanotechnology
- Materials science
- Sustainability
- Healthcare
- Engineering
External partners:
- SINTEF AS
- Institute for Energy Technology
- Equinor ASA
- Statkraft AS
- Kongsberg Maritime AS
- DNV AS