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

## TL;DR

This paper introduces a machine-learning-based design method to engineer biphoton spectra in nonlinear crystals, achieving high spectral purity for indistinguishable single photons across the telecommunication band, enhancing quantum communication capabilities.

## Contribution

It presents a novel machine-learning framework for customizing crystal poling profiles to produce spectrally uncorrelated biphotons over a broad spectral range, surpassing previous limitations.

## Key findings

- Achieved spectral purities over 99% in multiple telecommunication bands.
- Demonstrated the ability to generate near-indistinguishable photons without group-velocity matching.
- Validated the approach through numerical simulations with customized poling profiles.

## Abstract

Indistinguishable single photons are key ingredient 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 (SPDC) in nonlinear crystals. The purity of the SPDC biphotons, however, is limited by their spectral correlations. Here, we present a design recipe, based on a machine-learning framework, for the engineering of biphoton joint spectrum amplitudes over a wide spectral range. By customizing the poling profile of the KTiOPO$_4$ (KTP) crystal, we show, numerically, that spectral purities of 99.22%, 99.99%, and 99.82% can be achieved, respectively, in the 1310-nm, 1550-nm, 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 KTP crystal's group-velocity-matching wavelength window near 1582 nm.