Quantum annealing and the Schr\"odinger-Langevin-Kostin equation

We show, in the context of quantum combinatorial optimization, or quantum annealing, how the nonlinear Schr\"odinger-Langevin-Kostin equation can dynamically drive the system toward its ground state. We illustrate, moreover, how a frictional force of Kostin type can prevent the appearance of genuinely quantum problems such as Bloch oscillations and Anderson localization which would hinder an exhaustive search.Comment: 5 pages, 4 figures. To appear on Physical Review

A solvable model of quantum random optimization problems

We study the quantum version of a simplified model of optimization problems, where quantum fluctuations are introduced by a transverse field acting on the qubits. We find a complex low-energy spectrum of the quantum Hamiltonian, characterized by an abrupt condensation transition and a continuum of level crossings as a function of the transverse field. We expect this complex structure to have deep consequences on the behavior of quantum algorithms attempting to find solutions to these problems.Comment: 4 pages, 3 figures, accepted versio

Entropy generation in a model of reversible computation

We present a model in which, due to the quantum nature of the signals controlling the implementation time of successive unitary computational steps, \emph{physical} irreversibility appears in the execution of a \emph{logically} reversible computation.Comment: 13 pages, 6 figure

First-order transitions and the performance of quantum algorithms in random optimization problems

We present a study of the phase diagram of a random optimization problem in presence of quantum fluctuations. Our main result is the characterization of the nature of the phase transition, which we find to be a first-order quantum phase transition. We provide evidence that the gap vanishes exponentially with the system size at the transition. This indicates that the Quantum Adiabatic Algorithm requires a time growing exponentially with system size to find the ground state of this problem.Comment: 4 pages, 4 figures; final version accepted on Phys.Rev.Let

Histamine beyond its effects on allergy: Potential therapeutic benefits for the treatment of Amyotrophic Lateral Sclerosis (ALS).

ALS currently remains a challenge despite many efforts in performing successful clinical trials and formulating therapeutic solutions. By learning from current failures and striving for success, scientists and clinicians are checking every possibility to search for missing hints and efficacious treatments. Because the disease is very complex and heterogeneous and, moreover, targeting not only motor neurons but also several different cell types including muscle, glial, and immune cells, the right answer to ALS is conceivably a multidrug strategy or the use of broad-spectrum molecules. The aim of the present work is to gather evidence about novel perspectives on ALS pathogenesis and to present recent and innovative paradigms for therapy. In particular, we describe how an old molecule possessing immunomodulatory and neuroprotective functions beyond its recognized effects on allergy, histamine, might have a renewed and far-reaching momentum in ALS

Evolution of neurocontrollers in changing environments

One of the most challenging aspects of the control theory is the design and implementation of controllers that can deal with changing environments, i. e., non stationary systems. Quite good progress has been made on this area by using different kind of models: neural networks, fuzzy systems, evolutionary algorithms, etc. Our approach consists in the use of a memory based evolutionary algorithm, specially designed in such a way that can be used to evolve neurocontrollers to be applied in changing environments. In this paper, we describe our architecture, and present an example of its application to a typical control problem.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Prior knowledge in evolutionary fuzzy recurrent controllers design

A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI