Sensing an optical phase has been of central interest as it has been the fundamental theoretical and experimental model to illustrate the capabilities of utilizing quantum light to enhance the precision of estimation protocols and deliver tangible results to quantum technologies. It has been shown that the precision of estimation can exhibit significant scaling improvement when one uses light that cannot be predicted by the classical theory (e.g. squeezed light, entangled states). Applications of optical phase sensing is highly relevant to quantum communication as information is encoded in phase-modulated pulses of light, to force sensing, to quantum-enhanced imaging, and to magnetometry, just to name a few.
In this work, we elaborate on the precession limits in the relatively underexplored genuine Bayesian setting, i.e., when prior information, in the form of a probability distribution on the unknown parameter, is available. We prove that results from prior (non-Bayesian) literature are not necessarily applicable in our Bayesian setting and the whole subject must be revisited and analyzed properly. Additionally, using the powerful Bayes’ theorem, adaptive techniques find their natural space: Based on past measurements, one can enhance the precision by updating the prior probability on the unknown parameter for the subsequent sensing attempt.
Motivated by our theoretical discoveries and the applications thereof, a new research path is opening: exploring the optimal characteristics of quantum light and measurements, relevant to our quantum Bayesian sensing approach for estimating multiple parameters.
Click here to read our work on the arXiv.
Christos Gagatsos 