Quantum metrology is the study of making high-resolution and highly sensitive measurements of physical parameters using quantum theory to describe the physical systems, particularly exploiting quantum entanglement and quantum squeezing. This field promises to develop measurement techniques that give better precision than the same measurement performed in a classical framework.
One example of note is the use of the NOON state in a Mach-Zender interferometer to perform accurate phase measurements. A similar effect can be produced using less exotic states such as squeezed states. In atomic ensembles, spin squeezed states can be used for phase measurements.
An important application of particular note is the detection of gravitational radiation with projects such as LIGO. Here high precision distance measurements must be made of two widely separated masses. However, currently the measurements described by quantum metrology are usually not used as they are very difficult to implement and there are many other sources of noise which prohibit the detection of gravity waves which must be overcome first. Nevertheless, plans may call for the use of quantum metrology in LIGO.
Scaling and the effect of noise
A central question of quantum metrology, how the precision, i.e., the variance of the parameter estimation, scales with the number of particles. Classical interferometers cannot overcome the shot-noise limit where is the number of particles. Quantum metrology can reach the Heisenberg limit given by
Relation to quantum information science
There are strong links between quantum metrology and quantum information science. It has been shown that quantum entanglement is needed to outperform classical interferometry in magnetrometry with a fully polarized ensemble of spins. It has been proved that a similar relation is generally valid for any linear interferometer, independent of the details of the scheme. Moreover, higher and higher levels of multipartite entanglement is needed to achieve a better and better accuracy in parameter estimation.
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