Quantum Artificial Life in an IBM Quantum Computer

We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity

Algorithmic quantum simulation of memory effects

We propose a method for the algorithmic quantum simulation of memory effects described by integrodifferential evolution equations. It consists in the systematic use of perturbation theory techniques and a Markovian quantum simulator. Our method aims to efficiently simulate both completely positive and nonpositive dynamics without the requirement of engineering non-Markovian environments. Finally, we find that small error bounds can be reached with polynomially scaling resources, evaluated as the time required for the simulation

Quantum autoencoders via quantum adders with genetic algorithms

The quantum autoencoder is a recent paradigm in the field of quantum machine learning, which may enable an enhanced use of resources in quantum technologies. To this end, quantum neural networks with less nodes in the inner than in the outer layers were considered. Here, we propose a useful connection between approximate quantum adders and quantum autoencoders. Specifically, this link allows us to employ optimized approximate quantum adders, obtained with genetic algorithms, for the implementation of quantum autoencoders for a variety of initial states. Furthermore, we can also directly optimize the quantum autoencoders via genetic algorithms. Our approach opens a different path for the design of quantum autoencoders in controllable quantum platforms

Hybrid quantum-classical heuristic for the bin packing problem

Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.QUANTEK project (ELKARTEK program from the Basque Government, expedient no. KK-2021/00070) Spanish Ramón y Cajal Grant RYC-2020-030503- I QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies EU FET Open project Quromorphic (828826) and EPIQUS (899368

A Fungal Effector With Host Nuclear Localization and DNA-Binding Properties Is Required for Maize Anthracnose Development

Plant pathogens have the capacity to manipulate the host immune system through the secretion of effectors. We identified 27 putative effector proteins encoded in the genome of the maize anthracnose pathogen Colletotrichum graminicola that are likely to target the host’s nucleus, as they simultaneously contain sequence signatures for secretion and nuclear localization. We functionally characterized one protein, identified as CgEP1. This protein is synthesized during the early stages of disease development and is necessary for anthracnose development in maize leaves, stems, and roots. Genetic, molecular, and biochemical studies confirmed that this effector targets the host’s nucleus and defines a novel class of double-stranded DNA-binding protein. We show that CgEP1 arose from a gene duplication in an ancestor of a lineage of monocot-infecting Colletotrichum spp. and has undergone an intense evolution process, with evidence for episodes of positive selection. We detected CgEP1 homologs in several species of a grass-infecting lineage of Colletotrichum spp., suggesting that its function may be conserved across a large number of anthracnose pathogens. Our results demonstrate that effectors targeted to the host nucleus may be key elements for disease development and aid in the understanding of the genetic basis of anthracnose development in maize plants.Fil: Vargas, Walter Alberto. Universidad de Salamanca; España. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Sanz Martín, José M.. Universidad de Salamanca; EspañaFil: Rech, Gabriel E.. Universidad de Salamanca; EspañaFil: Armijos Jaramillo, Vinicio D.. Universidad de Salamanca; EspañaFil: Rivera Rodriguez, Lina Patricia. Universidad de Salamanca; EspañaFil: Echeverria, María de Las Mercedes. Universidad Nacional de Mar del Plata. Facultad de Ciencias Agrarias; ArgentinaFil: Díaz Mínguez, José M.. Universidad de Salamanca; EspañaFil: Thon, Michael R.. Universidad de Salamanca; EspañaFil: Sukno, Serenella A.. Universidad de Salamanca; Españ

Exploring complex causal pathways between urban renewal, health and health inequality using a theory-driven approach

Urban populations are growing and to accommodate these numbers, cities are becoming more involved in urban renewal programs to improve the physical, social and economic conditions in different areas. This paper explores some of the complexities surrounding the link between urban renewal, health and health inequalities using a theory-driven approach. ; We focus on an urban renewal initiative implemented in Barcelona, the Neighbourhoods Law, targeting Barcelona’s (Spain) most deprived neighbourhoods. We present evidence from two studies on the health evaluation of the Neighbourhoods Law, while drawing from recent urban renewal literature, to follow a four-step process to develop a program theory. We then use two specific urban renewal interventions, the construction of a large central plaza and the repair of streets and sidewalks, to further examine this link. ; In order for urban renewal programs to affect health and health inequality, neighbours must use and adapt to the changes produced by the intervention. However, there exist barriers that can result in negative outcomes including factors such as accessibility, safety and security. ; This paper provides a different perspective to the field that is largely dominated by traditional quantitative studies that are not always able to address the complexities such interventions provide. Furthermore, the framework and discussions serve as a guide for future research, policy development and evaluation