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

doi:10.1103/physrevapplied.12.034059

Cited by 31 publications

(18 citation statements)

References 62 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, artificial intelligence has gained popularity in optics due to its unique potential for handling complex classification and optimization tasks. [ 36–43 ] Indeed, machine learning has been used to engineer quantum states of light, [ 44,45 ] and to identify their properties in different degrees of freedom. [ 46,47 ] The use of machine learning is rapidly growing in multiple areas like quantum state tomography, quantum metrology, and optical communication.…”

Section: Introductionmentioning

confidence: 99%

Spatial Mode Correction of Single Photons Using Machine Learning

et al. 2021

View full text Add to dashboard Cite

Spatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum‐photonic technologies. Unfortunately, this problem is exacerbated at the single‐photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, the self‐learning and self‐evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes at the single‐photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. The results have important implications for real‐time turbulence correction of structured photons and single‐photon images.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatial Mode Correction of Single Photons Using Machine Learning

et al. 2021

View full text Add to dashboard Cite

show abstract

“…1a. The PPKTP crystal with type-II phase matching satisfies the groupvelocity-matching (GVM) condition at the telecom wavelength [18][19][20]. The polarization of the constituent photons is aligned along either the crystallographic y or z axis.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Quantum optical synthesis in 2D time-frequency space

Jin,

Tazawa,

Asamura

et al. 2020

Preprint

View full text Add to dashboard Cite

Conventional optical synthesis, the manipulation of the phase and amplitude of spectral components to produce an optical pulse in different temporal modes, is revolutionizing ultrafast optical science and metrology. These technologies rely on the Fourier transform of light fields between time and frequency domains in one-dimensional space. However, within this treatment it is impossible to incorporate the quantum correlation among photons. Here we expand the Fourier synthesis into high dimensional space to deal with the quantum correlation, and carry out an experimental demonstration by manipulating the two-photon probability distribution of a biphoton in two-dimensional time and frequency space. As a potential application, we show manipulation of a heralded single-photon wave packet, which is never explained by the conventional one-dimensional Fourier optics. Our approach opens up a new pathway to tailor the temporal characteristics of a biphoton wave packet with high dimensional quantum-mechanical treatment. We anticipate such high dimensional treatment of light in time and frequency domains could bridge the research fields between quantum optics and ultrafast optical measurements.

show abstract

“…In Fig. 7, the purities are 0.968, 0.963, and 0.826, respectively, and this purity can be further improved to near 1 using the custom poling technique [63,64].…”

Section: Discussionmentioning

confidence: 96%

Mid-infrared spectrally-uncorrelated biphotons generation from doped PPLN: a theoretical investigation

Wei,

Cai,

Ding

et al. 2020

Preprint

View full text Add to dashboard Cite

We theoretically investigate the preparation of mid-infrared (MIR) spectrally-uncorrelated biphotons from a spontaneous parametric down-conversion process using doped LN crystals, including MgO doped LN, ZnO doped LN, and In2O3 doped ZnLN with doping ratio from 0 to 7 mol%. The tilt angle of the phase-matching function and the corresponding poling period are calculated under type-II, type-I, and type-0 phase-matching conditions. We also calculate the thermal properties of the doped LN crystals and their performance in Hong-Ou-Mandel interference. It is found that the doping ratio has a substantial impact on the group-velocity-matching (GVM) wavelengths. Especially, the GVM2 wavelength of co-doped InZnLN crystal has a tunable range of 678.7 nm, which is much broader than the tunable range of less than 100 nm achieved by the conventional method of adjusting the temperature. It can be concluded that the doping ratio can be utilized as a degree of freedom to manipulate the biphoton state. The spectrally uncorrelated biphotons can be used to prepare pure single-photon source and entangled photon source, which may have promising applications for quantum-enhanced sensing, imaging, and communications at the MIR range.

show abstract

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

Cited by 31 publications

References 62 publications

Spatial Mode Correction of Single Photons Using Machine Learning

Spatial Mode Correction of Single Photons Using Machine Learning

Quantum optical synthesis in 2D time-frequency space

Mid-infrared spectrally-uncorrelated biphotons generation from doped PPLN: a theoretical investigation

Contact Info

Product

Resources

About