Architecture of the spatial mode quantum gate using diffractive deep neural networks (D²NNs). (IMAGE)
Caption
a Conceptual illustration of the D2NN quantum gate’s abstract architecture, showing how the neural networks map quantum states in high-dimensional Hilbert space to other states in that space. The specific three-dimensional X gate scheme is shown in the lower part as an example, including input states, multilayer D2NN, and output states. The mapping between inputs and outputs is indicated on the side of the D2NN, with the currently active mapping highlighted in yellow. b Experimental setup of a 4-layers spatial mode quantum gate. c A heralded single photon source is used. FPC: fiber polarization controller; Col.: collimator; SLM: spatial light modulator; L1-L6: lenses; BS: beam splitter; Cam.: camera; SPD1&2: single photon detector; C.C.: coincidence counting; PPLN1&2: periodically poled lithium niobate; SHG: second harmonic generation; SPDC: spontaneous parametric down-conversion; BPF: bandpass filter. While the heralding photons of the photon pairs are sent to the SPD1, the heralded photons are prepared into input states by complex modulation using SLM1, followed by D2NN-generated phase layers loaded onto SLM2 to perform the desired operation. Output states are analyzed using SLM3, a fiber coupling system, and the SPD2. The coincidence counting of SPD1&2 indicates the single photon passing the entire system. d Second-order correlation function g2(τ) of the heralded single photon source determined using the Hanbury Brown and Twiss setup. The g2(0) =0.024(2) implies the remarkable performance of the source.
Credit
by Qianke Wang, Jun Liu, Dawei Lyu, Jian Wang
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License
CC BY