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  Quantum sensing enhanced by machine learning


   Institute of Photonics and Quantum Sciences (IPaQS)

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  Dr Cristian Bonato  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Quantum sensors based on single spins are one of the most established quantum technologies. By using a single electronic spin associated with a point defect in diamond, we can map magnetic fields with nanoscale spatial resolution, under ambient conditions. This novel quantum imaging technology can impact different fundamental and applied fields, such as mapping of magnetic materials, imaging currents in novel materials [Nature 583, 537 (2020)] and integrated electronic chips [Phys Rev Appl 14,014097 (2020)]) or detecting chemical species in-vivo inside a cell [Nature Comms 8, 14701 (2017)]. These applications can have a great societal impact in terms of improving magnetic storage, electronic devices and biomedical diagnostics. Remarkably, spin-based quantum sensors can also detect individual nuclear spins, pushing magnetic resonance imaging to the ultimate limits of single molecule imaging [Nature 576, 411 (2019)].

Our group at Heriot-Watt University in Edinburgh is heavily involved in improving this technology and to apply it to relevant scientific and industrial problems. We have openings for PhD interested in joining our effort, in particular with respect to:

(1)  improving quantum sensing performance by using sequential Bayesian estimation and machine learning. One of the main hurdles for spin-based quantum sensing is the long signal acquisition times. In this project, we will use real-time adaptation of experimental parameters and machine learning to optimise quantum measurements to the ultimate limits. Our long-term goal is to develop AI-powered algorithms to design optimal adaptive control sequences and system identification tools (for example to detect single nuclear spins in nanoscale magnetic resonance). This work will be carried out in collaboration with the quantum theory group of Dr Erik Gauger and the signal processing group of Dr Yoann Altmann. We can accommodate projects with different levels of mixing between theory/numerical and experiments – however, proficiency in coding is a prerequisite in all cases.

(2)  Apply quantum sensing to the study of complex quantum materials. Our group has recently been awarded a >£2M grant to establish a Quantum Magnetometry facility that will utilise a single electronic spin to measure magnetic fields with nanoscale spatial resolution at ultra-low temperatures. This is a unique facility worldwide, which will open the way to the investigation of quantum correlated states in heterostructures of 2D materials (such as graphene and transition metal dichalcogenides), exotic magnetic textures, unconventional superconductivity. This work will be carried out in collaboration with Prof Brian Gerardot.

The group. The proposed research will be carried out in the Quantum Photonics Lab at Heriot-Watt University (Edinburgh, UK), under the supervision of Dr Cristian Bonato. The Quantum Photonics Lab works on quantum devices in different material platforms, such as diamond, SiC, “beyond graphene” 2D materials, rare-earth doped crystals and III-V semiconductors. The lab hosts state-of-the-art facilities, including several cryostats, superconducting single-photon detectors, tuneable lasers and radio-frequency equipment for high-fidelity spin manipulation. More information can be found on our website (https://qpl.eps.hw.ac.uk/). Please contact Cristian Bonato ([Email Address Removed]) as soon as possible for additional information.

Physics (29)

Funding Notes

EPSRC-funded DTP project, EPSRC and EU funding for equipment, travel and consumables

References

MJ Arshad et al, “Online adaptive estimation of decoherence timescales for a single qubit”, arxiv:2210.06103 (2022);
M Brotons-Gisbert at al, “Spin–layer locking of interlayer excitons trapped in moiré potentials”, Nature Materials 19, 630 (2020);
E Scerri et al, “Extending qubit coherence by adaptive quantum environment learning”, New Journal of Physics 22, 035002 (2020);
C Bonato et al, “Optimized quantum sensing with a single electron spin using real-time adaptive measurements”, Nature Nanotechnology 11, 247 (2016);