Thread by @fgrosshans

Now at #IWQNS Zhang Zheshen form @UoA on Entanglement-Assisted Quantum Photonic Sensor Networks
#LTQI

@uoa Zhang Zheshen uses enatgled light to detect targets. His entaglement source, low brightness limit: # photon/mode N_s ≫ 1. Phase correlation |⟨â_s â_I⟩|≈√N_S ≫ N_S. #LTQI #IWQNS

@uoa Zheshen Zhang:
In an experiment with 14 dB loss, 75 dB Nouise background, quantum wins by 1 dB
arxiv:1411.5969 arxiv.org/abs/1411.5969 PRL 114 110506 doi.org/10.1103/PhysRe…
Ideal implementation would be 3 dB
Quantum bound is 6 dB (matched by explicit receiver)
#IWQNS #LTQI

@uoa Zhang Zheshen moves on to entangled sensor networks, where M sensors estimate the same global parameter θ.
Unentangled: rms error ∝1/√M
Entangled : rms erro ∝ 1/M
#LTQI #IWQNS

@uoa Zhang Zheshen ∃ different scales of quantum network, form communication from 100 km to photonic chim with 100 nm.
#LTQI #IWQNS

Zhang Zheshen starts with a large scale quantum network, with CV, where a squeezed state is split between M locations. But losses are problematic.
arxiv:1711.10459 arxiv.org/abs/1711.10459
PRA 97 032329 doi.org/10.1103/PhysRe…
#LTQI #IWQNS

Zhang Zheshen fight the losses with a noiseless linear amplifier (NLA) at each measurement.
HE wants to implet it on a network testbed at @UoA, but also on small network
#LTQI #IWQNS

@uoa Zhang Zheshen also use machine learnig to encgineer optimal probe state arxiv:1901.09566 arxiv.org/abs/1901.09566 . The idea is to make the data classification dirrectly at the physical layer.
It also works in data compression scenario
#LTQI #IWQNS