Analysis, Modeling and Multi-Spectral Sensing for the Predictive Management of Verticillium Wilt in Olive Groves

The intensification and expansion in the cultivation of olives have contributed to the significant spread of Verticillium wilt, which is the most important fungal problem affecting olive trees. Recent studies confirm that practices such as the use of innovative natural minerals (Zeoshell ZF1) and the application of beneficial microorganisms (Micosat F BS WP) restore health in infected trees. However, for their efficient implementation the above methodologies require the marking of trees in the early stages of infestation—a task that is impractical with traditional means (manual labor) but also very difficult, as early stages are difficult to perceive with the naked eye. In this paper, we present the results of the My Olive Grove Coach (MyOGC) project, which used multispectral imaging from unmanned aerial vehicles to develop an olive grove monitoring system based on the autonomous and automatic processing of the multispectral images using computer vision and machine learning techniques. The goal of the system is to monitor and assess the health of olive groves, help in the prediction of Verticillium wilt spread and implement a decision support system that guides the farmer/agronomist

Robustness of STIRAP Shortcuts under Ornstein-Uhlenbeck Noise in the Energy Levels

In this article, we evaluate the efficiency of two shortcuts to adiabaticity for the STIRAP system, in the presence of Ornstein–Uhlenbeck noise in the energy levels. The shortcuts under consideration preserve the interactions of the original Hamiltonian, without adding extra counterdiabatic terms, which directly connect the initial and target states. The first shortcut is such that the mixing angle is a polynomial function of time, while the second shortcut is derived from Gaussian pulses. Extensive numerical simulations indicate that both shortcuts perform quite well and robustly even in the presence of relatively large noise amplitudes, while their performance is decreased with increasing noise correlation time. For similar pulse amplitudes and durations, the efficiency of classical STIRAP is highly degraded even in the absence of noise. When using pulses with similar areas for the two STIRAP shortcuts, the shortcut derived from Gaussian pulses appears to be more efficient. Since STIRAP is an essential tool for the implementation of emerging quantum technologies, the present work is expected to find application in this broad research field

A Review on Quantum Approximate Optimization Algorithm and its Variants

The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances it should be used. Towards this goal, we offer specific practical points in a form of a short guide. Keywords: Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Algorithms (VQAs), Quantum Optimization, Combinatorial Optimization Problems, NISQ AlgorithmsComment: 67 pages, 9 figures, 9 tables; version 2 -- added more discussions and practical guide