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

Abstract

For advanced quantum information technology, sources of photon pairs in quantum mechanically factorable states are of great importance for realizing high-fidelity photon-photon quantum gate operations. Here we experimentally demonstrate a technique to produce spectrally factorable photon pairs utilizing multi-order quasi-phase-matching (QPM) conditions in spontaneous parametric downconversion (SPDC). In our scheme, a spatial nonlinearity profile of a nonlinear optical crystal is shaped with current standard poling techniques, and the associated phase-matching function can be approximated to a Gaussian form. By the measurement of a phase-matching function and the second-order autocorrelation function, we demonstrate that telecom-band photon pairs produced by our custom-poled crystal are highly factorable with > 95% single-photon purity. I. INTRODUCTIONQuantum photonics technology plays an important role in various quantum applications such as quantum computing [1, 2], quantum communication [3,4], quantum metrology [5], and bridging solid and atomic quantum systems at a distance [6]. One of the central requirements in the quantum photonics technology is to produce pure single photons. Most multi-photon applications rely on interference of independent single photons [7], and only pure and indistinguishable photons can exhibit perfect interference. Photon-pair generation via spontaneous parametric downconversion (SPDC) has been widely accepted in quantum optics and photonic quantum information experi-

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.…”

Optimized design of the lithium niobate for spectrally-pure-state generation at MIR wavelengths using metaheuristic algorithm

“…Λ represents the poling period and A3 is the duty cycle at the 3 rd poling period. [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].…”

Design of mid-infrared entangled photon sources using lithium niobate

“…(1) the optimization of poling order: in 2011, Branczyk et al proposed and experimentally demonstrated the first optimization design of KTP by arranging the poling order [19], and this approach was further improved by Kaneda et al in 2021 [20]. (2) the optimization of duty cycle: in 2013, Dixon et al proposed to design the duty cycle of KTP [21], which was verified experimentally in 2017 [22]; In 2019, Cui et al adopted the Adam algorithm in a machine learning framework to optimize the duty cycle [23].…”