Improving the dynamics of quantum sensors with reinforcement learning

08/22/2019
by   Jonas Schuff, et al.
0

Recently proposed quantum-chaotic sensors achieve quantum enhancements in measurement precision by applying nonlinear control pulses to the dynamics of the quantum sensor while using classical initial states that are easy to prepare. Here, we use the cross entropy method of reinforcement learning to optimize the strength and position of control pulses. Compared to the quantum-chaotic sensors in the presence of superradiant damping, we find that decoherence can be fought even better and measurement precision can be enhanced further by optimizing the control. In some examples, we find enhancements in sensitivity by more than an order of magnitude. By visualizing the evolution of the quantum state, the mechanism exploited by the reinforcement learning method is identified as a kind of spin-squeezing strategy that is adapted to the superradiant damping.

READ FULL TEXT

page 13

page 26

10/21/2019

Deep Reinforcement Learning Control of Quantum Cartpoles

We generalize a standard benchmark of reinforcement learning, the classi...
11/19/2018

Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer

We present an experimental realization of a measurement-based adaptation...
08/22/2016

Single-shot Adaptive Measurement for Quantum-enhanced Metrology

Quantum-enhanced metrology aims to estimate an unknown parameter such th...
02/23/2021

Quantum Cross Entropy and Maximum Likelihood Principle

Quantum machine learning is an emerging field at the intersection of mac...
08/21/2023

Reinforcement Learning Based Sensor Optimization for Bio-markers

Radio frequency (RF) biosensors, in particular those based on inter-digi...
03/14/2018

Measurement-based adaptation protocol with quantum reinforcement learning

Machine learning employs dynamical algorithms that mimic the human capac...

Please sign up or login with your details

Forgot password? Click here to reset