427 research outputs found
One- and two-dimensional quantum walks in arrays of optical traps
We propose a novel implementation of discrete time quantum walks for a
neutral atom in an array of optical microtraps or an optical lattice. We
analyze a one-dimensional walk in position space, with the coin, the additional
qubit degree of freedom that controls the displacement of the quantum walker,
implemented as a spatially delocalized qubit, i.e., the coin is also encoded in
position space. We analyze the dependence of the quantum walk on temperature
and experimental imperfections as shaking in the trap positions. Finally,
combining a spatially delocalized qubit and a hyperfine qubit, we also give a
scheme to realize a quantum walk on a two-dimensional square lattice with the
possibility of implementing different coin operators.Comment: 10 pages, 8 figures; v2: some comments added and other minor change
Tripartite to Bipartite Entanglement Transformations and Polynomial Identity Testing
We consider the problem of deciding if a given three-party entangled pure
state can be converted, with a non-zero success probability, into a given
two-party pure state through local quantum operations and classical
communication. We show that this question is equivalent to the well-known
computational problem of deciding if a multivariate polynomial is identically
zero. Efficient randomized algorithms developed to study the latter can thus be
applied to the question of tripartite to bipartite entanglement
transformations
Thresholded Covering Algorithms for Robust and Max-Min Optimization
The general problem of robust optimization is this: one of several possible
scenarios will appear tomorrow, but things are more expensive tomorrow than
they are today. What should you anticipatorily buy today, so that the
worst-case cost (summed over both days) is minimized? Feige et al. and
Khandekar et al. considered the k-robust model where the possible outcomes
tomorrow are given by all demand-subsets of size k, and gave algorithms for the
set cover problem, and the Steiner tree and facility location problems in this
model, respectively.
In this paper, we give the following simple and intuitive template for
k-robust problems: "having built some anticipatory solution, if there exists a
single demand whose augmentation cost is larger than some threshold, augment
the anticipatory solution to cover this demand as well, and repeat". In this
paper we show that this template gives us improved approximation algorithms for
k-robust Steiner tree and set cover, and the first approximation algorithms for
k-robust Steiner forest, minimum-cut and multicut. All our approximation ratios
(except for multicut) are almost best possible.
As a by-product of our techniques, we also get algorithms for max-min
problems of the form: "given a covering problem instance, which k of the
elements are costliest to cover?".Comment: 24 page
Decision and function problems based on boson sampling
Boson sampling is a mathematical problem that is strongly believed to be
intractable for classical computers, whereas passive linear interferometers can
produce samples efficiently. So far, the problem remains a computational
curiosity, and the possible usefulness of boson-sampling devices is mainly
limited to the proof of quantum supremacy. The purpose of this work is to
investigate whether boson sampling can be used as a resource of decision and
function problems that are computationally hard, and may thus have
cryptographic applications. After the definition of a rather general
theoretical framework for the design of such problems, we discuss their
solution by means of a brute-force numerical approach, as well as by means of
non-boson samplers. Moreover, we estimate the sample sizes required for their
solution by passive linear interferometers, and it is shown that they are
independent of the size of the Hilbert space.Comment: Close to the version published in PR
Green function approach for scattering quantum walks
In this work a Green function approach for scattering quantum walks is
developed. The exact formula has the form of a sum over paths and always can be
cast into a closed analytic expression for arbitrary topologies and position
dependent quantum amplitudes. By introducing the step and path operators, it is
shown how to extract any information about the system from the Green function.
The method relevant features are demonstrated by discussing in details an
example, a general diamond-shaped graph.Comment: 13 pages, 6 figures, this article was selected by APS for Virtual
Journal of Quantum Information, Vol 11, Iss 11 (2011
Stochastic Budget Optimization in Internet Advertising
Internet advertising is a sophisticated game in which the many advertisers
"play" to optimize their return on investment. There are many "targets" for the
advertisements, and each "target" has a collection of games with a potentially
different set of players involved. In this paper, we study the problem of how
advertisers allocate their budget across these "targets". In particular, we
focus on formulating their best response strategy as an optimization problem.
Advertisers have a set of keywords ("targets") and some stochastic information
about the future, namely a probability distribution over scenarios of cost vs
click combinations. This summarizes the potential states of the world assuming
that the strategies of other players are fixed. Then, the best response can be
abstracted as stochastic budget optimization problems to figure out how to
spread a given budget across these keywords to maximize the expected number of
clicks.
We present the first known non-trivial poly-logarithmic approximation for
these problems as well as the first known hardness results of getting better
than logarithmic approximation ratios in the various parameters involved. We
also identify several special cases of these problems of practical interest,
such as with fixed number of scenarios or with polynomial-sized parameters
related to cost, which are solvable either in polynomial time or with improved
approximation ratios. Stochastic budget optimization with scenarios has
sophisticated technical structure. Our approximation and hardness results come
from relating these problems to a special type of (0/1, bipartite) quadratic
programs inherent in them. Our research answers some open problems raised by
the authors in (Stochastic Models for Budget Optimization in Search-Based
Advertising, Algorithmica, 58 (4), 1022-1044, 2010).Comment: FINAL versio
Boosting search by rare events
Randomized search algorithms for hard combinatorial problems exhibit a large
variability of performances. We study the different types of rare events which
occur in such out-of-equilibrium stochastic processes and we show how they
cooperate in determining the final distribution of running times. As a
byproduct of our analysis we show how search algorithms are optimized by random
restarts.Comment: 4 pages, 3 eps figures. References update
Worst case and probabilistic analysis of the 2-Opt algorithm for the TSP
2-Opt is probably the most basic local search heuristic for the TSP. This heuristic achieves amazingly good results on âreal worldâ Euclidean instances both with respect to running time and approximation ratio. There are numerous experimental studies on the performance of 2-Opt. However, the theoretical knowledge about this heuristic is still very limited. Not even its worst case running time on 2-dimensional Euclidean instances was known so far. We clarify this issue by presenting, for every pâN , a family of L p instances on which 2-Opt can take an exponential number of steps.
Previous probabilistic analyses were restricted to instances in which n points are placed uniformly at random in the unit square [0,1]2, where it was shown that the expected number of steps is bounded by O~(n10) for Euclidean instances. We consider a more advanced model of probabilistic instances in which the points can be placed independently according to general distributions on [0,1] d , for an arbitrary dâ„2. In particular, we allow different distributions for different points. We study the expected number of local improvements in terms of the number n of points and the maximal density Ï of the probability distributions. We show an upper bound on the expected length of any 2-Opt improvement path of O~(n4+1/3â
Ï8/3) . When starting with an initial tour computed by an insertion heuristic, the upper bound on the expected number of steps improves even to O~(n4+1/3â1/dâ
Ï8/3) . If the distances are measured according to the Manhattan metric, then the expected number of steps is bounded by O~(n4â1/dâ
Ï) . In addition, we prove an upper bound of O(Ïâd) on the expected approximation factor with respect to all L p metrics.
Let us remark that our probabilistic analysis covers as special cases the uniform input model with Ï=1 and a smoothed analysis with Gaussian perturbations of standard deviation Ï with ÏâŒ1/Ï d
The effect of large-decoherence on mixing-time in Continuous-time quantum walks on long-range interacting cycles
In this paper, we consider decoherence in continuous-time quantum walks on
long-range interacting cycles (LRICs), which are the extensions of the cycle
graphs. For this purpose, we use Gurvitz's model and assume that every node is
monitored by the corresponding point contact induced the decoherence process.
Then, we focus on large rates of decoherence and calculate the probability
distribution analytically and obtain the lower and upper bounds of the mixing
time. Our results prove that the mixing time is proportional to the rate of
decoherence and the inverse of the distance parameter (\emph{m}) squared.
This shows that the mixing time decreases with increasing the range of
interaction. Also, what we obtain for \emph{m}=0 is in agreement with
Fedichkin, Solenov and Tamon's results \cite{FST} for cycle, and see that the
mixing time of CTQWs on cycle improves with adding interacting edges.Comment: 16 Pages, 2 Figure
- âŠ