2,072 research outputs found
Fast Quantum Algorithm for Solving Multivariate Quadratic Equations
In August 2015 the cryptographic world was shaken by a sudden and surprising
announcement by the US National Security Agency NSA concerning plans to
transition to post-quantum algorithms. Since this announcement post-quantum
cryptography has become a topic of primary interest for several standardization
bodies. The transition from the currently deployed public-key algorithms to
post-quantum algorithms has been found to be challenging in many aspects. In
particular the problem of evaluating the quantum-bit security of such
post-quantum cryptosystems remains vastly open. Of course this question is of
primarily concern in the process of standardizing the post-quantum
cryptosystems. In this paper we consider the quantum security of the problem of
solving a system of {\it Boolean multivariate quadratic equations in
variables} (\MQb); a central problem in post-quantum cryptography. When ,
under a natural algebraic assumption, we present a Las-Vegas quantum algorithm
solving \MQb{} that requires the evaluation of, on average,
quantum gates. To our knowledge this is the fastest algorithm for solving
\MQb{}
Estimating proportions of objects from multispectral scanner data
Progress is reported in developing and testing methods of estimating, from multispectral scanner data, proportions of target classes in a scene when there are a significiant number of boundary pixels. Procedures were developed to exploit: (1) prior information concerning the number of object classes normally occurring in a pixel, and (2) spectral information extracted from signals of adjoining pixels. Two algorithms, LIMMIX and nine-point mixtures, are described along with supporting processing techniques. An important by-product of the procedures, in contrast to the previous method, is that they are often appropriate when the number of spectral bands is small. Preliminary tests on LANDSAT data sets, where target classes were (1) lakes and ponds, and (2) agricultural crops were encouraging
Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly
There has been considerable progress in the design and construction of
quantum annealing devices. However, a conclusive detection of quantum speedup
over traditional silicon-based machines remains elusive, despite multiple
careful studies. In this work we outline strategies to design hard tunable
benchmark instances based on insights from the study of spin glasses - the
archetypal random benchmark problem for novel algorithms and optimization
devices. We propose to complement head-to-head scaling studies that compare
quantum annealing machines to state-of-the-art classical codes with an approach
that compares the performance of different algorithms and/or computing
architectures on different classes of computationally hard tunable spin-glass
instances. The advantage of such an approach lies in having to only compare the
performance hit felt by a given algorithm and/or architecture when the instance
complexity is increased. Furthermore, we propose a methodology that might not
directly translate into the detection of quantum speedup, but might elucidate
whether quantum annealing has a "`quantum advantage" over corresponding
classical algorithms like simulated annealing. Our results on a 496 qubit
D-Wave Two quantum annealing device are compared to recently-used
state-of-the-art thermal simulated annealing codes.Comment: 14 pages, 8 figures, 3 tables, way too many reference
The dendritic cell algorithm for intrusion detection
As one of the solutions to intrusion detection problems, Artificial Immune
Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is
no one archetypal AIS, instead there are four major paradigms. Among them, the
Dendritic Cell Algorithm (DCA) has produced promising results in various
applications. The aim of this chapter is to demonstrate the potential for the
DCA as a suitable candidate for intrusion detection problems. We review some of
the commonly used AIS paradigms for intrusion detection problems and
demonstrate the advantages of one particular algorithm, the DCA. In order to
clearly describe the algorithm, the background to its development and a formal
definition are given. In addition, improvements to the original DCA are
presented and their implications are discussed, including previous work done on
an online analysis component with segmentation and ongoing work on automated
data preprocessing. Based on preliminary results, both improvements appear to
be promising for online anomaly-based intrusion detection.Comment: Bio-Inspired Communications and Networking, IGI Global, 84-102, 201
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