Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories that are in most cases best fit by composite correlated random walks with features that resemble Lévy walks. This composite motion emerges from the collective behavior developed under the specific foraging selection pressures. On the other hand, agents trained to reach nearby resources predominantly exhibit Brownian trajectories.

Background Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defence. To do so, honeybees use an alarm pheromone that recruits nearby bees into mass stinging of the perceived threat. This alarm pheromone is carried directly on the stinger; hence, its concentration builds up during the course of the attack. We investigate how bees react to different alarm pheromone concentrations and how this evolved response pattern leads to better coordination at the group level. Results We first present a dose-response curve to the alarm pheromone, obtained experimentally. This data reveals two phases in the bees’ response: initially, bees become more likely to sting as the alarm pheromone concentration increases, but aggressiveness drops back when very high concentrations are reached. Second, we apply Projective Simulation to model each bee as an artificial learning agent that relies on the pheromone concentration to decide whether to sting or not. Individuals are rewarded based on the collective performance, thus emulating natural selection in these complex societies. By also modelling predators in a detailed way, we are able to identify the main selection pressures that shaped the response pattern observed experimentally. In particular, the likelihood to sting in the absence of alarm pheromone (starting point of the dose-response curve) is inversely related to the rate of false alarms, such that bees in environments with low predator density are less likely to waste efforts responding to irrelevant stimuli. This is compensated for by a steep increase in aggressiveness when the alarm pheromone concentration starts rising. The later decay in aggressiveness may be explained as a curbing mechanism preventing worker loss. Conclusions Our work provides a detailed understanding of alarm pheromone responses in honeybees and sheds light on the selection pressures that brought them about. In addition, it establishes our approach as a powerful tool to explore how selection based on a collective outcome shapes individual responses, which remains a challenging issue in the field of evolutionary biology.

We introduce a game to illustrate the principles of quantum mechanics using a qubit (or spin-first) approach, where students can experience and discover its puzzling features first-hand. Students take the role of particles and scientists. Scientists unravel underlying rules and properties by collecting and analysing data that is generated by observing particles that act according to given rules. We show how this allows one to illustrate quantum states, their stochastic behavior under measurements as well as quantum entanglement. In addition, we use this approach to illustrate and discuss decoherence, and a modern application of quantum features, namely quantum cryptography. We have tested the game in class and report on the results we obtained.

Recent progress in the development of quantum technologies, most notably in the context of quantum computing and cryptography, poses the question of whether and how to teach the modern and sophisticated underlying theory of quantum physics, and its applications, at school. To this end, we present a game-based approach to teach Bell inequalities and quantum cryptography at undergraduate level. The approach is based on kinesthetic activities and allows students to experience and discover quantum features and their applications first-hand. The students obtain the same results as if they were in a real laboratory performing sophisticated experiments such as Bell tests, without requiring expensive tools and facilities. Specifically, quantum states are represented by the orientation of students, who also play the role of quantum particles, and mimic quantitative random behaviour and measurements using dice and apps.

We study the effect of local decoherence on arbitrary quantum states. Adapting techniques developed in quantum metrology, we show that the action of generic local noise processes --though arbitrarily small-- always yields a state whose Quantum Fisher Information (QFI) with respect to local observables is linear in system size N, independent of the initial state. This implies that all macroscopic quantum states, which are characterized by a QFI that is quadratic in N, are fragile under decoherence, and cannot be maintained if the system is not perfectly isolated. We also provide analytical bounds on the effective system size, and show that the effective system size scales as the inverse of the noise parameter p for small p for all the noise channels considered, making it increasingly difficult to generate macroscopic or even mesoscopic quantum states. In turn, we also show that the preparation of a macroscopic quantum state, with respect to a conserved quantity, requires a device whose QFI is already at least as large as the one of the desired state. Given that the preparation device itself is classical and not a perfectly isolated macroscopic quantum state, the preparation device needs to be quadratically bigger than the macroscopic target state.