3,367 research outputs found
Fidelity Between Unitary Operators and the Generation of Gates Robust Against Off-Resonance Perturbations
We perform a functional expansion of the fidelity between two unitary
matrices in order to find the necessary conditions for the robust
implementation of a target gate. Comparison of these conditions with those
obtained from the Magnus expansion and Dyson series shows that they are
equivalent in first order. By exploiting techniques from robust design
optimization, we account for issues of experimental feasibility by introducing
an additional criterion to the search for control pulses. This search is
accomplished by exploring the competition between the multiple objectives in
the implementation of the NOT gate by means of evolutionary multi-objective
optimization
Exploring, tailoring, and traversing the solution landscape of a phase-shaped CARS process
Pulse shaping techniques are used to improve the selectivity of broadband CARS experiments, and to reject the overwhelming background. Knowledge about the fitness landscape and the capability of tailoring it is crucial for both fundamental insight and performing an efficient optimization of phase shapes. We use an evolutionary algorithm to find the optimal spectral phase of the broadband pump and probe beams in a background-suppressed shaped CARS process. We then investigate the shapes, symmetries, and topologies of the landscape contour lines around the optimal solution and also around the point corresponding to zero phase. We demonstrate the significance of the employed phase bases in achieving convex contour lines, suppressed local optima, and high optimization fitness with a few (and even a single) optimization parameter
Information driven self-organization of complex robotic behaviors
Information theory is a powerful tool to express principles to drive
autonomous systems because it is domain invariant and allows for an intuitive
interpretation. This paper studies the use of the predictive information (PI),
also called excess entropy or effective measure complexity, of the sensorimotor
process as a driving force to generate behavior. We study nonlinear and
nonstationary systems and introduce the time-local predicting information
(TiPI) which allows us to derive exact results together with explicit update
rules for the parameters of the controller in the dynamical systems framework.
In this way the information principle, formulated at the level of behavior, is
translated to the dynamics of the synapses. We underpin our results with a
number of case studies with high-dimensional robotic systems. We show the
spontaneous cooperativity in a complex physical system with decentralized
control. Moreover, a jointly controlled humanoid robot develops a high
behavioral variety depending on its physics and the environment it is
dynamically embedded into. The behavior can be decomposed into a succession of
low-dimensional modes that increasingly explore the behavior space. This is a
promising way to avoid the curse of dimensionality which hinders learning
systems to scale well.Comment: 29 pages, 12 figure
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
- …