49,341 research outputs found
Pair Partitioning in time reversal acoustics
Time reversal of acoustic waves can be achieved efficiently by the persistent
control of excitations in a finite region of the system. The procedure, called
Time Reversal Mirror, is stable against the inhomogeneities of the medium and
it has numerous applications in medical physics, oceanography and
communications. As a first step in the study of this robustness, we apply the
Perfect Inverse Filter procedure that accounts for the memory effects of the
system. In the numerical evaluation of such procedures we developed the Pair
Partitioning method for a system of coupled oscillators. The algorithm,
inspired in the Trotter strategy for quantum dynamics, obtains the dynamic for
a chain of coupled harmonic oscillators by the separation of the system in
pairs and applying a stroboscopic sequence that alternates the evolution of
each pair. We analyze here the formal basis of the method and discuss his
extension for including energy dissipation inside the medium.Comment: 6 pages, 4 figure
A model of adaptive decision making from representation of information environment by quantum fields
We present the mathematical model of decision making (DM) of agents acting in
a complex and uncertain environment (combining huge variety of economical,
financial, behavioral, and geo-political factors). To describe interaction of
agents with it, we apply the formalism of quantum field theory (QTF). Quantum
fields are of the purely informational nature. The QFT-model can be treated as
a far relative of the expected utility theory, where the role of utility is
played by adaptivity to an environment (bath). However, this sort of
utility-adaptivity cannot be represented simply as a numerical function. The
operator representation in Hilbert space is used and adaptivity is described as
in quantum dynamics. We are especially interested in stabilization of solutions
for sufficiently large time. The outputs of this stabilization process,
probabilities for possible choices, are treated in the framework of classical
DM. To connect classical and quantum DM, we appeal to Quantum Bayesianism
(QBism). We demonstrate the quantum-like interference effect in DM which is
exhibited as a violation of the formula of total probability and hence the
classical Bayesian inference scheme.Comment: in press in Philosophical Transactions
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions
We put forward a strategy to encode a quantum operation into the unmodulated
dynamics of a quantum network without the need of external control pulses,
measurements or active feedback. Our optimization scheme, inspired by
supervised machine learning, consists in engineering the pairwise couplings
between the network qubits so that the target quantum operation is encoded in
the natural reduced dynamics of a network section. The efficacy of the proposed
scheme is demonstrated by the finding of uncontrolled four-qubit networks that
implement either the Toffoli gate, the Fredkin gate, or remote logic
operations. The proposed Toffoli gate is stable against imperfections, has a
high-fidelity for fault tolerant quantum computation, and is fast, being based
on the non-equilibrium dynamics.Comment: 8 pages, 3 figure
High fidelity quantum memory via dynamical decoupling: theory and experiment
Quantum information processing requires overcoming decoherence---the loss of
"quantumness" due to the inevitable interaction between the quantum system and
its environment. One approach towards a solution is quantum dynamical
decoupling---a method employing strong and frequent pulses applied to the
qubits. Here we report on the first experimental test of the concatenated
dynamical decoupling (CDD) scheme, which invokes recursively constructed pulse
sequences. Using nuclear magnetic resonance, we demonstrate a near order of
magnitude improvement in the decay time of stored quantum states. In
conjunction with recent results on high fidelity quantum gates using CDD, our
results suggest that quantum dynamical decoupling should be used as a first
layer of defense against decoherence in quantum information processing
implementations, and can be a stand-alone solution in the right parameter
regime.Comment: 6 pages, 3 figures. Published version. This paper was initially
entitled "Quantum gates via concatenated dynamical decoupling: theory and
experiment", by Jacob R. West, Daniel A. Lidar, Bryan H. Fong, Mark F. Gyure,
Xinhua Peng, and Dieter Suter. That original version split into two papers:
http://arxiv.org/abs/1012.3433 (theory only) and the current pape
A quantum genetic algorithm with quantum crossover and mutation operations
In the context of evolutionary quantum computing in the literal meaning, a
quantum crossover operation has not been introduced so far. Here, we introduce
a novel quantum genetic algorithm which has a quantum crossover procedure
performing crossovers among all chromosomes in parallel for each generation. A
complexity analysis shows that a quadratic speedup is achieved over its
classical counterpart in the dominant factor of the run time to handle each
generation.Comment: 21 pages, 1 table, v2: typos corrected, minor modifications in
sections 3.5 and 4, v3: minor revision, title changed (original title:
Semiclassical genetic algorithm with quantum crossover and mutation
operations), v4: minor revision, v5: minor grammatical corrections, to appear
in QI
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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