Quantum state transfer protocols are a major toolkit in many quantum information processing tasks, from quantum key distribution to quantum computation. To assess performance of a such a protocol, one often relies on the average fidelity between the input and the output states. Going beyond this scheme, we analyze the entire probability distribution of fidelity, providing a general framework to derive it for the transfer of single- and two-qubit states. Starting from the delta-like shape of the fidelity distribution, characteristic to perfect transfer, we analyze its broadening and deformation due to realistic features of the process, including non-perfect read-out timing. Different models of quantum transfer, sharing the same value of the average fidelity, display different distributions of fidelity, providing thus additional information on the protocol, including the minimum fidelity.

We investigate machine learning-based noise classification aimed at the recognition of the Markovian character of a dynamics and the identification of correlations of classical noise, as well as their interplay in small quantum networks. We operate control based on Coherent Tunneling by Adiabatic Passage (CTAP) or Stimulated Raman Adiabatic Passage (STIRAP) in a three-level system using different pulse configurations as inputs to train a feedforward neural network. Our results show that supervised learning can classify distinct types of classical diagonal noise affecting the system. Three non-Markovian (quasistatic correlated, anti-correlated, and uncorrelated) and Markovian noise mechanisms are classified with $99%$ accuracy. Instead, correlations of Markovian noises cannot be classified with our method. The approach is robust against statistical measurement errors keeping its effectiveness even for physical measurements where a limited number of samples is available.

The recently proposed zero-added-loss multiplexing (ZALM) source of entangled photons enables higher efficiency in entanglement distribution than SPDC sources and can be carried out using both space-to-ground and ground-to-ground links. We demonstrate the flexibility of ZALM architectures to be adapted to alternative entanglement distribution protocols. Focusing on the counter-intuitive result that entanglement can be generated between distant parties without using any entanglement as a resource, we analyze two protocols for entanglement distribution to memories via separable states. Modelling them in a ZALM setup, we consider the effects of noise both in the communication channels and in the memories. We thereby identify the optimal protocol to use, with respect to the highest entanglement generated, given the noise conditions of the network.

Recent developments have led to the possibility of embedding machine learning tools into experimental platforms to address key problems, including the characterization of the properties of quantum states. Leveraging on this, we implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon. The underlying reservoir dynamics through which such input state evolves is implemented using the coined quantum walk of high-dimensional photonic orbital angular momentum and performing projective measurements over a fixed basis. We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections, thus representing a promising route for resource-economic state characterization.