Abstract
Quantum metrology plays a fundamental role in many scientific areas. However, the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter with given resources. Here, we address this problem by introducing adjustable controls into the encoding process and then utilizing a hybrid quantum-classical approach to automatically optimize the controls online. Our scheme does not require any complex or intractable off-line design, and it can inherently correct certain unitary errors during the learning procedure. We also report the first experimental demonstration of this promising scheme for the task of finding optimal probes for frequency estimation on a nuclear magnetic resonance (NMR) processor. The proposed scheme paves the way to experimentally auto-search optimal protocol for improving the metrology precision.
Highlights
Quantum metrology plays a fundamental role in many scientific areas
We presented a demonstrative experiment of finding optimal probes for estimating the frequency by hqc-Gradient Ascent Pulse Engineering (GRAPE) on a two-qubit nuclear magnetic resonance (NMR) processor
In order to demonstrate the advantages of hqc-GRAPE in searching optimal protocol for quantum metrology in realistic experiments, we compared it with the conventional open-loop designs entirely running on classical computer, which we marked gl according to the ideal Hamiltonian, as GRAPE. which does
Summary
Quantum metrology plays a fundamental role in many scientific areas. the complexity of engineering entangled probes and the external noise raise technological barriers for realizing the expected precision of the to-be-estimated parameter with given resources. The experimental results verify the success of hqc-GRAPE in learning optimal controls for improving the metrology precision. The metrology process using hqc-GRAPE begins with some prepared pure probe state ρ0 , which does not need to be optimal.
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