Wave-Function Engineering for Spectrally Uncorrelated Biphotons in the Telecommunication Band Based on a Machine-Learning Framework

Chaohan Cui, Reeshad Arian, Saikat Guha, N. Peyghambarian, Quntao Zhuang, Zheshen Zhang

Research output: Contribution to journalArticle

Abstract

Indistinguishable single photons are key ingredients for a plethora of quantum-information-processing applications, ranging from quantum communications to photonic quantum computing. A mainstream platform to produce indistinguishable single photons over a wide spectral range is based on biphoton generation through spontaneous parametric down-conversion in nonlinear crystals. The purity of the biphotons produced is, however, limited by their spectral correlations. Here we present a design recipe, based on a machine-learning framework, for the engineering of biphoton joint spectral amplitudes over a wide spectral range. By customizing the poling profile of the KTiOPO4 crystal, we show, numerically, that spectral purities of 99.22%, 99.99%, and 99.82%, respectively, can be achieved in the 1310-, 1550-, and 1600-nm bands after applying a moderate 8-nm filter. The machine-learning framework thus enables the generation of near-indistinguishable single photons over the entire telecommunication band without resorting to the KTiOPO4 crystal's group-velocity-matching wavelength window near 1582 nm.

Original languageEnglish (US)
Article number034059
JournalPhysical Review Applied
Volume12
Issue number3
DOIs
StatePublished - Sep 30 2019

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machine learning
telecommunication
wave functions
engineering
purity
photons
crystals
spectral correlation
quantum communication
quantum computation
ingredients
group velocity
platforms
photonics
filters
profiles
wavelengths

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Wave-Function Engineering for Spectrally Uncorrelated Biphotons in the Telecommunication Band Based on a Machine-Learning Framework. / Cui, Chaohan; Arian, Reeshad; Guha, Saikat; Peyghambarian, N.; Zhuang, Quntao; Zhang, Zheshen.

In: Physical Review Applied, Vol. 12, No. 3, 034059, 30.09.2019.

Research output: Contribution to journalArticle

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