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

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.