DeepAI
Log In Sign Up

Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

03/04/2020
by   Lukas J. Fiderer, et al.
0

Quantum metrology promises unprecedented measurement precision but suffers in practice from the limited availability of resources such as the number of probes, their coherence time, or non-classical quantum states. The adaptive Bayesian approach to parameter estimation allows for an efficient use of resources thanks to adaptive experiment design. For its practical success fast numerical solutions for the Bayesian update and the adaptive experiment design are crucial. Here we show that neural networks can be trained to become fast and strong experiment-design heuristics using a combination of an evolutionary strategy and reinforcement learning. Neural-network heuristics are shown to outperform established heuristics for the technologically important example of frequency estimation of a qubit that suffers from dephasing. Our method of creating neural-network heuristics is very general and complements the well-studied sequential Monte-Carlo method for Bayesian updates to form a complete framework for adaptive Bayesian quantum estimation.

READ FULL TEXT

page 5

page 13

page 14

09/01/2022

Deep reinforcement learning for quantum multiparameter estimation

Estimation of physical quantities is at the core of most scientific rese...
02/16/2022

Simplified algorithms for adaptive experiment design in parameter estimation

In experiments to estimate parameters of a parametric model, Bayesian ex...
02/08/2021

Quantum machine learning with adaptive linear optics

We study supervised learning algorithms in which a quantum device is use...
07/15/2019

Experimental machine learning quantum homodyne tomography

Complete characterization of states and processes that occur within quan...
03/03/2021

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

We introduce Deep Adaptive Design (DAD), a method for amortizing the cos...
03/13/2019

Variational Estimators for Bayesian Optimal Experimental Design

Bayesian optimal experimental design (BOED) is a principled framework fo...
11/03/2021

Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods

We introduce implicit Deep Adaptive Design (iDAD), a new method for perf...