TY - JOUR
T1 - Resource-efficient adaptive Bayesian tracking of magnetic fields with a quantum sensor
AU - Craigie, K. L.
AU - Gauger, E. M.
AU - Altmann, Y.
AU - Bonato, C.
N1 - Funding Information:
This project is supported by the Engineering and Physical Sciences Research council through Grants EP/S000550/1 and EP/T01377X/1, and by a Weizmann-UK Joint Research Programme grant (Grant agreement 125215). KC acknowledges studentship funding from EPSRC under Grant No. EP/L015110/1. YA is supported by the Royal Academy of Engineering under the Research Fellowship scheme RF201617/16/31.
Publisher Copyright:
© 2021 The Author(s). Published by IOP Publishing Ltd.
PY - 2021/5/12
Y1 - 2021/5/12
N2 - Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. For, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.
AB - Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation can speed up the sensing process by reducing the number of measurements required. These protocols consist of computing and updating the probability distribution of the magnetic field based on measurement outcomes and of determining optimized acquisition settings for the next measurement. However, the computational steps feeding into the measurement settings of the next iteration must be performed quickly enough to allow real-time updates. This article addresses the issue of computational speed by implementing an approximate Bayesian estimation technique, where probability distributions are approximated by a finite sum of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian density, we find that in many cases, the magnetic field probability distribution can be described by fewer than ten parameters, achieving a reduction in computation time by factor 10 compared to existing approaches. For, only a small decrease in computation time is achieved. However, in these regimes, the proposed Gaussian protocol outperforms the existing one in tracking accuracy.
KW - Bayesian filtering
KW - magnetic field tracking
KW - nitrogen-vacancy centre
KW - quantum metrology
UR - http://www.scopus.com/inward/record.url?scp=85105532238&partnerID=8YFLogxK
U2 - 10.1088/1361-648X/abe34f
DO - 10.1088/1361-648X/abe34f
M3 - Article
C2 - 33540392
SN - 0953-8984
VL - 33
JO - Journal of Physics: Condensed Matter
JF - Journal of Physics: Condensed Matter
IS - 19
M1 - 195801
ER -