Quantum Reports

The Earth’s geomagnetic field (GMF) is a fundamental environmental signal for plants, with its perception rooted in quantum biology. Specifically, the radical pair mechanism (RPM) explains how this weak force influences electron spin states in metabolic pathways, providing a framework for its profound biological impact. Research shows that a hypomagnetic field (hMF) directly reduces the production of reactive oxygen species (ROS), creating a quantum signature in plants. This is a counterintuitive finding, as it suggests the plant perceives less oxidative stress and, in response, downregulates its antioxidant defenses. This multi-level effect, from a quantum trigger to molecular and metabolic changes, ultimately affects the plant’s growth and phenotype. This review suggests a possible link between the GMF and plant health, identifying the GMF as a potential physiological modulator. Manipulating the magnetic field could therefore be a novel strategy for improving crop resilience and growth. However, the fact that some effects cannot be fully explained by the RPM suggests other quantum mechanisms are involved, paving the way for future research into these undiscovered processes and their potential inheritance across generations.

The nearest convex hull (NCH) classifier is a promising algorithm for the classification of biosignals, such as electroencephalography (EEG) signals, especially when adapted to the classification of symmetric positive definite matrices. In this paper, we implemented a version of this classifier that can execute either on a traditional computer or a quantum simulator, and we tested it against state-of-the-art classifiers for EEG classification. This article addresses the practical challenges of adapting a classical algorithm to one that can be executed on a quantum computer or a quantum simulator. One of these challenges is to find a formulation of the classification problem that is quadratic, is binary, and accepts only linear constraints—that is, an objective function that can be solved using a variational quantum algorithm. In this article, we present two approaches to solve this problem, both compatible with continuous variables. Finally, we evaluated, for the first time, the performance of the NCH classifier on real EEG data using both quantum and classical optimization methods. We selected a particularly challenging dataset, where classical optimization typically performs poorly, and demonstrated that the nearest convex hull classifier was able to generalize with a modest performance. One lesson from this case study is that, by separating the objective function from the solver, it becomes possible to allow an existing classical algorithm to run on a quantum computer, as long as an appropriate objective function—quadratic and binary—can be found.

Due to the important role of quantum information technology in the future development of science and technology, researchers have extensively studied the preparation, characterization, and application of quantum systems. It is of great significance to further study the universality and generalization of multi-qubit entangled states. Especially in quantum communication, the actual quantum system is always affected by various noises from the environment. Noise has a significant impact on the properties of the actual quantum system, so we study the effects of noise on a prepared two-photon four-qubit state by two methods. We experimentally simulated the most common bit-flip noise in quantum systems. The law of evolution of the fidelity of two-dimensional four-qubit states and violation of the Mermin inequality and the Ardehali inequality for LR under different levels of bit-flip noise are investigated. The experimental results show that entanglement fidelity and nonlocality can be used to judge the degree of noise interference in the quantum channel and, thus, judge the security of the quantum communication channel. This judgment is of great significance for the realization of practical long-distance multi-node quantum communication.