Distributing quantum information between remote systems will necessitate the integration of
emerging quantum components with existing communication infrastructure. This requires …

Black-box zero-th order optimization is a central primitive for applications in fields as diverse
as finance, physics, and engineering. In a common formulation of this problem, a designer …

Urban flood risk mapping has become crucial for effective mitigation and urban planning.
This study assesses and maps flood risk in the city of Zaio, Morocco, using machine learning …

Second-order photon correlation measurements [g (2)(τ) functions] are widely used to
classify single-photon emission purity in quantum emitters or to measure the multiexciton …

We provide a tool for measuring the Stokes parameters and the degree of polarization of
single photons by employing second-order interference, namely, the Hong-Ou-Mandel …

The preparation of quantum states is crucial for enabling quantum computations and
simulations. In this work, we present a general framework for preparing ground states of …

Optimal design for model training is a critical topic in machine learning. Active learning aims
at obtaining improved models by querying samples with maximum uncertainty according to …

We propose an optimization algorithm to improve the design and performance of quantum
communication networks. When physical architectures become too complex for analytical …

Quantum measurements that use the entangled photons' polarization to encode quantum
information require calibration and alignment of the measurement bases between spatially …

5G advancements like Massive Multiple Input Multiple Output (MIMO) bring high capacity
and low latency, but also intensify interference challenges. Static and dynamic coordination …