Critical Quantum Metrology Assisted by Real-Time Feedback Control

We investigate critical quantum metrology, that is, the estimation of parameters in many-body systems close to a quantum critical point, through the lens of Bayesian inference theory. We first derive a no-go result stating that any nonadaptive strategy will fail to exploit quantum critical enhancement (i.e., precision beyond the shot-noise limit) for a sufficiently large number of particles N whenever our prior knowledge is limited. We then consider different adaptive strategies that can overcome this no-go result and illustrate their performance in the estimation of (i) a magnetic field using a probe of 1D spin Ising chain and (ii) the coupling strength in a Bose-Hubbard square lattice. Our results show that adaptive strategies with real-time feedback control can achieve sub-shot-noise scaling even with few measurements and substantial prior uncertainty.

Automated summary

In this study, we investigate the estimation of parameters in many-body systems near a quantum critical point, known as critical quantum metrology, using Bayesian inference theory. We demonstrate a no-go result which states that nonadaptive strategies will fail to exploit quantum critical enhancement when the prior knowledge is limited. However, we propose adaptive strategies with real-time feedback control that can overcome this limitation and achieve sub-shot-noise scaling even with few measurements and substantial prior uncertainty.