We introduce a general model for a network of quantum sensors, and we use this model to consider the question: when do correlations (quantum or classical) between quantum sensors enhance the precision with which the network can measure an unknown set of parameters? We rigorously answer this question for a range of practically important problems. When each sensor in the network measures a single parameter, we show that correlations between sensors cannot increase the estimation precision beyond what can be achieved with an uncorrelated scheme, regardless of the particular details of the estimation problem in question. We also consider the more general setting whereby each sensor may be used to measure multiple parameters, e.g., the three spatial components of a magnetic field. In this case, we show that correlations between sensors can only provide, at best, a small constant precision enhancement, over uncorrelated estimation techniques. Finally, we consider optimizing the network for measuring a single linear function of the unknown parameters, e.g., the average of all of the parameters. Here quantum correlations between the sensors can provide a significant precision enhancement over uncorrelated techniques, and this enhancement factor scales with the number of sensors. To illustrate the broad implications of this work, we apply our results to a wide range of estimation problems of practical interest, including multi-mode optical interferometry, networks of atomic sensors, and networks of clocks. Our findings shed light on a number of results in the literature, provide a rigorous general framework for future research on networked quantum sensors, and have implications for both quantum multi-parameter estimation theory, and quantum sensing technologies.
[1]
Reports on Progress in Physics
,
1934
.
[2]
G. G. Stokes.
"J."
,
1890,
The New Yale Book of Quotations.
[3]
M. Tsang.
Multiparameter Heisenberg limit
,
2014,
1403.4080.
[4]
Zachary Eldredge,et al.
Optimal and secure measurement protocols for quantum sensor networks.
,
2016,
Physical review. A.
[5]
K.Venkatesh Prasad,et al.
Fundamentals of statistical signal processing: Estimation theory: by Steven M. KAY; Prentice Hall signal processing series; Prentice Hall; Englewood Cliffs, NJ, USA; 1993; xii + 595 pp.; $65; ISBN: 0-13-345711-7
,
1994
.
[6]
J. Koenderink.
Q…
,
2014,
Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.
[7]
J. Kołodyński,et al.
Quantum limits in optical interferometry
,
2014,
1405.7703.
[8]
Augusto Smerzi,et al.
Quantum-enhanced multiparameter estimation in multiarm interferometers
,
2015,
Scientific Reports.
[9]
Thierry Paul,et al.
Quantum computation and quantum information
,
2007,
Mathematical Structures in Computer Science.
[10]
Carl D. Meyer,et al.
Matrix Analysis and Applied Linear Algebra
,
2000
.
[11]
C. Helstrom.
Quantum detection and estimation theory
,
1969
.
[12]
Ilgaitis Prūsis,et al.
Nature of Photon
,
2019
.
[13]
W. Marsden.
I and J
,
2012
.
[14]
Srihari Keshavamurthy,et al.
Annual Review of Physical Chemistry, 2015
,
2016
.
[15]
R. Bhatia.
Positive Definite Matrices
,
2007
.
[16]
Tillmann Baumgratz,et al.
Multi-parameter quantum metrology
,
2016,
1604.02615.
[17]
Zach DeVito,et al.
Opt
,
2017
.