Broadband photon pairs are highly desirable for quantum metrology, quantum sensing, and quantum communication. Such sources are usually designed through type-0 phase-matching spontaneous parametric down-conversion (SPDC) that makes the photon pairs hard to separate in the frequency-degenerate case and thus limits their applications. In this paper, we design a broadband frequency-degenerate telecom-band photon pair source via the type-II SPDC in a dispersion-engineered thin-film lithium niobate waveguide, where the polarization modes of photon pairs are orthogonal and thus are easily separated deterministically. With a 5-mm-long waveguide, our design can achieve a bandwidth of 5.56 THz (44.8 nm), which is 8.6 times larger than that of the bulk lithium niobate, and the central wavelength can be flexibly adjusted. Our design is a promising approach towards high-quality integrated photon sources and may have wide applications in photonic quantum technologies.

In principle, the asynchronous measurement-device-independent quantum key distribution (AMDI-QKD) can surpass the key rate capacity without phase tracking and phase locking. However, practical imperfections in sources or detections would dramatically depress its performance. Here, we present an improved model on AMDI-QKD to reduce the influence of these imperfections, including intensity fluctuation, the afterpulse effect, and the dead time of detectors. Furthermore, we carry out corresponding numerical simulations. Simulation results show that, by implementing our present work, it can have more than 100 km longer secure transmission distance and one order of magnitude enhancement in the key generation rate after 320 km compared with the standard method. Moreover, our model can still break the Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound even under realistic experimental conditions.

Neural networks are becoming ubiquitous in various areas of physics as a successful machine learning (ML) technique for addressing different tasks. Based on ML technique, we propose and experimentally demonstrate an efficient method for state reconstruction of the widely used Sagnac polarization-entangled photon source. By properly modeling the target states, a multi-output fully connected neural network is well trained using only six of the sixteen measurement bases in standard tomography technique, and hence our method reduces the resource consumption without loss of accuracy. We demonstrate the ability of the neural network to predict state parameters with a high precision by using both simulated and experimental data. Explicitly, the mean absolute error for all the parameters is below 0.05 for the simulated data and a mean fidelity of 0.99 is achieved for experimentally generated states. Our method could be generalized to estimate other kinds of states, as well as other quantum information tasks.