Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.Comment: 17 pages, 8 figures. Minor further revisions. As published in Phys. Rev.

Desmantelamiento óptimo de redes delincuenciales. Una perspectiva desde el modelado matemático y computacional

This work deals with the study and comparison of different strategies for the optimal dismantling of delinquent networks, which aim to optimally identify the most relevant individuals in the network. The strategy of greater complexity that we have studied here, is based on the Katz-Bonacich centrality criteria as a measure of influence of the individuals in the network. This results in an NP-hard type of problem, therefore, in order to apply that criteria, we must use heuristic methods which allow us to find approximate solutions. In particular, the methods used in this work are the Monte Carlo and greedy algorithms. We compared their performance against less sophisticated strategies and we were able to find that these algorithms perform relatively better, which contributes to improve our understanding of these approaches. In addition, we discuss a model that was recently introduced, which justifies the use of Katz-Bonacich centrality from the point of view of game theory on networks.El objetivo de este trabajo es estudiar y comparar diferentes estrategias para el desmantelamiento óptimo de redes delincuenciales, las cuales están representadas en algoritmos que permiten la identificación óptima de los individuos claves en la red. La estrategia de mayor complejidad se basa en la métrica de centralidad de Katz-Bonacich como medida de influencia en la red, y da lugar a un problema NP-difícil por lo que se debe recurrir a métodos heurísticos para encontrar soluciones aproximadas. Aquí se desarrolla un algoritmo basado en el método Monte Carlo y se compara con un método basado en algoritmos voraces introducido recientemente en la literatura. En este trabajo se compara además el desempeño de éstos con estrategias menos sofisticadas y se proporciona evidencia que dichos algoritmos se desempeñan relativamente bien, contribuyendo así a proporcionar un mejor entendimiento de éstos. Se discute además un modelo introducido recientemente que justifica el uso de la centralidad de Katz-Bonacich desde el punto de vista de la teoría de juegos sobre redes

Evolutionary advantages of turning points in human cooperative behaviour.

Cooperation is crucial to overcome some of the most pressing social challenges of our times, such as the spreading of infectious diseases, corruption and environmental conservation. Yet, how cooperation emerges and persists is still a puzzle for social scientists. Since human cooperation is individually costly, cooperative attitudes should have been eliminated by natural selection in favour of selfishness. Yet, cooperation is common in human societies, so there must be some features which make it evolutionarily advantageous. Using a cognitive inspired model of human cooperation, recent work Realpe-Gómez (2018) has reported signatures of criticality in human cooperative groups. Theoretical evidence suggests that being poised at a critical point provides evolutionary advantages to groups by enhancing responsiveness of these systems to external attacks. After showing that signatures of criticality can be detected in human cooperative groups composed by Moody Conditional Cooperators, in this work we show that being poised close to a turning point enhances the fitness and make individuals more resistant to invasions by free riders

Learning Dynamics and Norm Psychology Supports Human Cooperation in a Large-Scale Prisoner’s Dilemma on Networks

In this work, we explore the role of learning dynamics and social norms in human cooperation on networks. We study the model recently introduced in [Physical Review E, 97, 042321 (2018)] that integrates the well-studied Experience Weighted Attraction learning model with some features characterizing human norm psychology, namely the set of cognitive abilities humans have evolved to deal with social norms. We provide further evidence that this extended model—that we refer to as Experience Weighted Attraction with Norm Psychology—closely reproduces cooperative patterns of behavior observed in large-scale experiments with humans. In particular, we provide additional support for the finding that, when deciding to cooperate, humans balance between the choice that returns higher payoffs with the choice in agreement with social norms. In our experiment, agents play a prisoner’s dilemma game on various network structures: (i) a static lattice where agents have a fixed position; (ii) a regular random network where agents have a fixed position; and (iii) a dynamic lattice where agents are randomly re-positioned at each game iteration. Our results show that the network structure does not affect the dynamics of cooperation, which corroborates results of prior laboratory experiments. However, the network structure does seem to affect how individuals balance between their self-interested and normative choices