56 research outputs found
A Note on Easy and Efficient Computation of Full Abelian Periods of a Word
Constantinescu and Ilie (Bulletin of the EATCS 89, 167-170, 2006) introduced
the idea of an Abelian period with head and tail of a finite word. An Abelian
period is called full if both the head and the tail are empty. We present a
simple and easy-to-implement -time algorithm for computing all
the full Abelian periods of a word of length over a constant-size alphabet.
Experiments show that our algorithm significantly outperforms the
algorithm proposed by Kociumaka et al. (Proc. of STACS, 245-256, 2013) for the
same problem.Comment: Accepted for publication in Discrete Applied Mathematic
String Periods in the Order-Preserving Model
The order-preserving model (op-model, in short) was introduced quite recently but has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time O(n), O(n log log n), O(n log^2 log n/log log log n), O(n log n) depending on the type of periodicity. In the most general variant the number of different periods can be as big as Omega(n^2), and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of such periods
Quantum algorithms for problems in number theory, algebraic geometry, and group theory
Quantum computers can execute algorithms that sometimes dramatically
outperform classical computation. Undoubtedly the best-known example of this is
Shor's discovery of an efficient quantum algorithm for factoring integers,
whereas the same problem appears to be intractable on classical computers.
Understanding what other computational problems can be solved significantly
faster using quantum algorithms is one of the major challenges in the theory of
quantum computation, and such algorithms motivate the formidable task of
building a large-scale quantum computer. This article will review the current
state of quantum algorithms, focusing on algorithms for problems with an
algebraic flavor that achieve an apparent superpolynomial speedup over
classical computation.Comment: 20 pages, lecture notes for 2010 Summer School on Diversities in
Quantum Computation/Information at Kinki Universit
Algorithms for Computing Abelian Periods of Words
Constantinescu and Ilie (Bulletin EATCS 89, 167--170, 2006) introduced the
notion of an \emph{Abelian period} of a word. A word of length over an
alphabet of size can have distinct Abelian periods.
The Brute-Force algorithm computes all the Abelian periods of a word in time
using space. We present an off-line
algorithm based on a \sel function having the same worst-case theoretical
complexity as the Brute-Force one, but outperforming it in practice. We then
present on-line algorithms that also enable to compute all the Abelian periods
of all the prefixes of .Comment: Accepted for publication in Discrete Applied Mathematic
On the Parikh-de-Bruijn grid
We introduce the Parikh-de-Bruijn grid, a graph whose vertices are
fixed-order Parikh vectors, and whose edges are given by a simple shift
operation. This graph gives structural insight into the nature of sets of
Parikh vectors as well as that of the Parikh set of a given string. We show its
utility by proving some results on Parikh-de-Bruijn strings, the abelian analog
of de-Bruijn sequences.Comment: 18 pages, 3 figures, 1 tabl
String periods in the order-preserving model
In the order-preserving model, two strings match if they share the same relative order between the characters at the corresponding positions. This model is quite recent, but it has already attracted significant attention because of its applications in data analysis. We introduce several types of periods in this setting (op-periods). Then we give algorithms to compute these periods in time O(n), O(nloglogn), O(nlog2logn/logloglogn), O(nlogn) depending on the type of periodicity. In the most general variant, the number of different op-periods can be as big as Ω(n2), and a compact representation is needed. Our algorithms require novel combinatorial insight into the properties of op-periods. In particular, we characterize the Fine–Wilf property for coprime op-periods. © 2019 Elsevier Inc.Supported by ISF grants no. 824/17 and 1278/16 and by an ERC grant MPM under the EU's Horizon 2020 Research and Innovation Programme (grant no. 683064).Supported by the Ministry of Science and Higher Education of the Russian Federation, project 1.3253.2017.A part of this work was done during the workshop StringMasters in Warsaw 2017 that was sponsored by the Warsaw Center of Mathematics and Computer Science. The authors thank the participants of the workshop, especially Hideo Bannai and Shunsuke Inenaga, for helpful discussions
Quantum algorithms for algebraic problems
Quantum computers can execute algorithms that dramatically outperform
classical computation. As the best-known example, Shor discovered an efficient
quantum algorithm for factoring integers, whereas factoring appears to be
difficult for classical computers. Understanding what other computational
problems can be solved significantly faster using quantum algorithms is one of
the major challenges in the theory of quantum computation, and such algorithms
motivate the formidable task of building a large-scale quantum computer. This
article reviews the current state of quantum algorithms, focusing on algorithms
with superpolynomial speedup over classical computation, and in particular, on
problems with an algebraic flavor.Comment: 52 pages, 3 figures, to appear in Reviews of Modern Physic
Quantum Computing: Lecture Notes
This is a set of lecture notes suitable for a Master's course on quantum
computation and information from the perspective of theoretical computer
science. The first version was written in 2011, with many extensions and
improvements in subsequent years. The first 10 chapters cover the circuit model
and the main quantum algorithms (Deutsch-Jozsa, Simon, Shor, Hidden Subgroup
Problem, Grover, quantum walks, Hamiltonian simulation and HHL). They are
followed by 3 chapters about complexity, 4 chapters about distributed ("Alice
and Bob") settings, and a final chapter about quantum error correction.
Appendices A and B give a brief introduction to the required linear algebra and
some other mathematical and computer science background. All chapters come with
exercises, with some hints provided in Appendix C.Comment: 184 pages. Version 2: added a new chapter about QMA and local
Hamiltonian, more exercises in several chapters, and some small
corrections/clarification
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