Speaker
Description
We summarize our recent work on quantum chaotic sensors, machine
learning in quantum metrology, and Bayesian experimental design and
data analysis.
Measurement devices have traditionally always used integrable systems
as sensors, such as precessing spins or harmonic oscillators prepared
in non-classical states. But large benefits can be drawn from
rendering integrable quantum sensors chaotic, both in terms of
achievable sensitivity as well as robustness to noise [1].
After demonstrating the principles at the hand of the "kicked top", we
apply the method to spin-precession magnetometry and show that the
sensitivity of state-of-the-art magnetometers can be further enhanced
by subjecting the spin-precession to non-linear kicks realized with
off-resonant laser pulses that render the dynamics chaotic [2].
Further drastic improvements can be achieved by optimizing the
individual kicking strengths with reinforcement learning [3]. More
generally, we discuss the benefits of Bayesian experimental design and
data analysis in the context of quantum metrology [4,5].
[1] Quantum metrology with quantum-chaotic sensors, Lukas J. Fiderer
and Daniel Braun, Nature Communications 9, 1351 (2018).
[2] A quantum-chaotic cesium-vapor magnetometer, Lukas J. Fiderer and
Daniel Braun, Conf. Proceedings "Optical, Opto-Atomic, and
Entanglement-Enhanced Precision Metrology", 10934, 10934S (2019);
arXiv:1903.02393 [quant-ph]
[3] Improving the dynamics of quantum sensors with reinforcement
learning, Jonas Schuff, Lukas J. Fiderer, and Daniel Braun, NJP 22,
035001 (2020).
[4] Neural-Network Heuristics for Adaptive Bayesian Quantum
Estimation, Lukas J. Fiderer, Jonas Schuff, and Daniel Braun, PRX Quantum 2, 020303 (2021)
[5] Principles of quantum functional testing, Nadia Milazzo, Olivier
Giraud, Giovanni Gramegna, and Daniel Braun, arXiv:2209.11712
Email Address of submitter
daniel.braun@uni-tuebingen.de
| Poster printing | Yes |
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