20 research outputs found
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
The main advantage of Constraint Programming (CP) approaches for sequential
pattern mining (SPM) is their modularity, which includes the ability to add new
constraints (regular expressions, length restrictions, etc). The current best
CP approach for SPM uses a global constraint (module) that computes the
projected database and enforces the minimum frequency; it does this with a
filtering algorithm similar to the PrefixSpan method. However, the resulting
system is not as scalable as some of the most advanced mining systems like
Zaki's cSPADE. We show how, using techniques from both data mining and CP, one
can use a generic constraint solver and yet outperform existing specialized
systems. This is mainly due to two improvements in the module that computes the
projected frequencies: first, computing the projected database can be sped up
by pre-computing the positions at which an symbol can become unsupported by a
sequence, thereby avoiding to scan the full sequence each time; and second by
taking inspiration from the trailing used in CP solvers to devise a
backtracking-aware data structure that allows fast incremental storing and
restoring of the projected database. Detailed experiments show how this
approach outperforms existing CP as well as specialized systems for SPM, and
that the gain in efficiency translates directly into increased efficiency for
other settings such as mining with regular expressions.Comment: frequent sequence mining, constraint programmin
Prefix-Projection Global Constraint for Sequential Pattern Mining
Sequential pattern mining under constraints is a challenging data mining
task. Many efficient ad hoc methods have been developed for mining sequential
patterns, but they are all suffering from a lack of genericity. Recent works
have investigated Constraint Programming (CP) methods, but they are not still
effective because of their encoding. In this paper, we propose a global
constraint based on the projected databases principle which remedies to this
drawback. Experiments show that our approach clearly outperforms CP approaches
and competes well with ad hoc methods on large datasets
Implementation of the Five Qubit Error Correction Benchmark
The smallest quantum code that can correct all one-qubit errors is based on
five qubits. We experimentally implemented the encoding, decoding and
error-correction quantum networks using nuclear magnetic resonance on a five
spin subsystem of labeled crotonic acid. The ability to correct each error was
verified by tomography of the process. The use of error-correction for
benchmarking quantum networks is discussed, and we infer that the fidelity
achieved in our experiment is sufficient for preserving entanglement.Comment: 6 pages with figure
Quantum Simulation of Tunneling in Small Systems
A number of quantum algorithms have been performed on small quantum
computers; these include Shor's prime factorization algorithm, error
correction, Grover's search algorithm and a number of analog and digital
quantum simulations. Because of the number of gates and qubits necessary,
however, digital quantum particle simulations remain untested. A contributing
factor to the system size required is the number of ancillary qubits needed to
implement matrix exponentials of the potential operator. Here, we show that a
set of tunneling problems may be investigated with no ancillary qubits and a
cost of one single-qubit operator per time step for the potential evolution. We
show that physically interesting simulations of tunneling using 2 qubits (i.e.
on 4 lattice point grids) may be performed with 40 single and two-qubit gates.
Approximately 70 to 140 gates are needed to see interesting tunneling dynamics
in three-qubit (8 lattice point) simulations.Comment: 4 pages, 2 figure
Digital Quantum Simulation of the Statistical Mechanics of a Frustrated Magnet
Many interesting problems in physics, chemistry, and computer science are
equivalent to problems of interacting spins. However, most of these problems
require computational resources that are out of reach by classical computers. A
promising solution to overcome this challenge is to exploit the laws of quantum
mechanics to perform simulation. Several "analog" quantum simulations of
interacting spin systems have been realized experimentally. However, relying on
adiabatic techniques, these simulations are limited to preparing ground states
only. Here we report the first experimental results on a "digital" quantum
simulation on thermal states; we simulated a three-spin frustrated magnet, a
building block of spin ice, with an NMR quantum information processor, and we
are able to explore the phase diagram of the system at any simulated
temperature and external field. These results serve as a guide for identifying
the challenges for performing quantum simulation on physical systems at finite
temperatures, and pave the way towards large scale experimental simulations of
open quantum systems in condensed matter physics and chemistry.Comment: 7 pages for the main text plus 6 pages for the supplementary
material
Quantum Computing
Quantum mechanics---the theory describing the fundamental workings of
nature---is famously counterintuitive: it predicts that a particle can be in
two places at the same time, and that two remote particles can be inextricably
and instantaneously linked. These predictions have been the topic of intense
metaphysical debate ever since the theory's inception early last century.
However, supreme predictive power combined with direct experimental observation
of some of these unusual phenomena leave little doubt as to its fundamental
correctness. In fact, without quantum mechanics we could not explain the
workings of a laser, nor indeed how a fridge magnet operates. Over the last
several decades quantum information science has emerged to seek answers to the
question: can we gain some advantage by storing, transmitting and processing
information encoded in systems that exhibit these unique quantum properties?
Today it is understood that the answer is yes. Many research groups around the
world are working towards one of the most ambitious goals humankind has ever
embarked upon: a quantum computer that promises to exponentially improve
computational power for particular tasks. A number of physical systems,
spanning much of modern physics, are being developed for this task---ranging
from single particles of light to superconducting circuits---and it is not yet
clear which, if any, will ultimately prove successful. Here we describe the
latest developments for each of the leading approaches and explain what the
major challenges are for the future.Comment: 26 pages, 7 figures, 291 references. Early draft of Nature 464, 45-53
(4 March 2010). Published version is more up-to-date and has several
corrections, but is half the length with far fewer reference
An Efficient Algorithm for Mining Frequent Sequence with Constraint Programming
The main advantage of Constraint Programming (CP) approaches for sequential pattern mining (SPM) is their modularity, which includes the ability to add new constraints (regular expressions, length restrictions, etc.). The current best CP approach for SPM uses a global constraint (module) that computes the projected database and enforces the minimum frequency; it does this with a filtering algorithm similar to the PrefixSpan method. However, the resulting system is not as scalable as some of the most advanced mining systems like Zakiâs cSPADE. We show how, using techniques from both data mining and CP, one can use a generic constraint solver and yet outperform existing specialized systems. This is mainly due to two improvements in the module that computes the projected frequencies: first, computing the projected database can be sped up by pre-computing the positions at which a symbol can become unsupported by a sequence, thereby avoiding to scan the full sequence each time; and second by taking inspiration from the trailing used in CP solvers to devise a backtracking-aware data structure that allows fast incremental storing and restoring of the projected database. Detailed experiments show how this approach outperforms existing CP as well as specialized systems for SPM, and that the gain in efficiency translates directly into increased efficiency for other settings such as mining with regular expressions. The data and software related to this paper are available at http://âsites.âuclouvain.âbe/âcp4dm/âspm/â
Declarative Sequential Pattern Mining of Care Pathways
International audienceSequential pattern mining algorithms are widely used to explore care pathways database, but they generate a deluge of patterns, mostly redundant or useless. Clinicians need tools to express complex mining queries in order to generate less but more significant patterns. These algorithms are not versatile enough to answer complex clinician queries. This article proposes to apply a declarative pattern mining approach based on Answer Set Programming paradigm. It is exemplified by a pharmaco-epidemiological study investigating the possible association between hospitalization for seizure and antiepileptic drug switch from a french medico-administrative database
A Global Constraint for Mining Sequential Patterns with {GAP} Constraint
International audienc
Clustering Formulation Using Constraint Optimization
The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intracluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach