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  Adaptive Learning for Quantum Sensing

   Institute of Photonics and Quantum Sciences (IPaQS)

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  Dr Cristian Bonato  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

Short project description: We have an opening for a motivated student to perform theoretical work related to optimising quantum sensing protocols to enhance sensitivity and minimise measurement times sensing, using machine learning techniques. The target application is the extension of nuclear magnetic resonance to the nanoscale domain.

Long description:

This project will build on recent experimental and theoretical work showing that sensors based on a single spin can detect the tiny magnetic field of isolated proximal nuclear spins. The precision of these nanoscale sensors can be boosted by quantum effects. This constitutes an exciting new frontier in Nuclear Magnetic Resonance (NMR), a phenomenon that has found numerous applications across science and medicine, most notably MRI scanning. Developing optimised control approaches and sensing protocols will be tackled through a number of techniques including e.g. genetic algorithms, Bayesian approaches, machine learning, and “Hamiltonian learning”.

 The project will be carried out as a collaboration between the Quantum Photonics Lab and the Quantum Theory Team at Heriot-Watt University, Edinburgh. The Quantum Photonics Lab ( consists of a team of about 20 researchers and students, working on quantum devices in different material platforms (SiC, diamond, III-V semiconductors, 2D heterostructures, rare earth doped crystals). The Quantum Theory Team ( encompasses about ten people focussing on experimentally relevant analytical and computational research ranging from open quantum systems, (bio-inspired) quantum technologies, light-matter interactions, to quantum information theory and metrology.

   Applicants should have, or expect to obtain, a 1st Class Honours degree in a relevant discipline, for example Physics, Computer Science or Electrical Engineering. Please contact Dr Cristian Bonato ([Email Address Removed]) and Erik Gauger ([Email Address Removed]) for additional information.

Computer Science (8) Physics (29)


C. Bonato et al, "Optimised quantum sensing with a single electron spin using real-time adaptive measurements" Nature Nanotechnology 11, 247 (2016)
E. Scerri at al, "Extending qubit coherence by adaptive quantum environment learning", New Journal of Physics 22, 035002 (2020)
T. Joas et al, "Online Adaptive Quantum Characterization of a Nuclear Spin" (to appear in NPJ Quantum Information, 2021)
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 About the Project