Spectral characterization of photon-pair sources via classical sum-frequency generation

Abstract

Tailoring spectral properties of photon pairs is of great importance for optical quantum information and measurement applications. High-resolution spectral measurement is a key technique for engineering spectral properties of photons, making them ideal for various quantum applications. Here we demonstrate spectral measurements and optimization of frequency-entangled photon pairs produced via spontaneous parametric downconversion (SPDC), utilizing frequency-resolved sum-frequency generation (SFG), the reverse process of SPDC. A joint phase-matching spectrum of a nonlinear crystal around 1580 nm is captured with a 40 pm resolution and a > 40 dB signal-to-noise ratio, which is significantly improved compared to traditional frequency-resolved coincidence measurements. Moreover, our scheme is applicable to collinear degenerate sources whose characterization is difficult with previously demonstrated stimulated difference frequency generation (DFG). We also illustrate that the observed phase-matching function is useful for finding an optimal pump spectrum to maximize the spectral indistinguishability of SPDC photons. We expect that our precise spectral characterization technique will be useful tool for characterizing and tailoring SPDC sources for a wide range of optical quantum applications.

Generation of spectrally factorable photon pairs via multi-order quasi-phase-matched spontaneous parametric downconversion

“…In order to directly measure the phase-matching function of our CPKTP crystal, we performed frequency-resolved sum-frequency generation (SFG) measurement [36], using a slightly modified setup as shown in Fig. 3 where the signal and idler wavelengths (λ s , λ i ) are anti-correlated: 1/λ s = 1/λ p − 1/λ i , λ p = 775 nm.…”

Optimized Design of the Lithium Niobate for Spectrally‐Pure‐State Generation at MIR Wavelengths Using Metaheuristic Algorithm

“…The first one is to modulate the poling order, that is, lower‐order poling in the middle and higher‐order poling at the sides of the crystal. [ 34,35 ] The second one is to modulate the duty cycle with a near‐ideal Gaussian error function, [ 36,37 ] which could be realized using the machine‐learning framework method. [ 38 ] Noted that machine learning technology is developing in many fields, such as both classical device [ 39 ] and quantum ones.…”

Frequency-to-Time Mapping Technique for Direct Spectral Characterization of Biphoton States From Pulsed Spontaneous Parametric Processes

“…In another approach, which relies on Fourier spectroscopy, a very low spectral resolution paired with measurement instability and long integration time to reconstruct the JSI can be identified (Wasilewski et al, 2006;Eckstein et al, 2014). In methods based on stimulated SPDC or SFWM, despite the contribution of seed light in providing high generation rates and hence enabling a fast implementation, the stimulated emission itself appears as a source of noise due to the spatial and spectral overlap with photonpairs originating from the parametric process (Eckstein et al, 2014;Jizan et al, 2015;Kaneda et al, 2020).…”