64,648 research outputs found
Semantic distillation: a method for clustering objects by their contextual specificity
Techniques for data-mining, latent semantic analysis, contextual search of
databases, etc. have long ago been developed by computer scientists working on
information retrieval (IR). Experimental scientists, from all disciplines,
having to analyse large collections of raw experimental data (astronomical,
physical, biological, etc.) have developed powerful methods for their
statistical analysis and for clustering, categorising, and classifying objects.
Finally, physicists have developed a theory of quantum measurement, unifying
the logical, algebraic, and probabilistic aspects of queries into a single
formalism. The purpose of this paper is twofold: first to show that when
formulated at an abstract level, problems from IR, from statistical data
analysis, and from physical measurement theories are very similar and hence can
profitably be cross-fertilised, and, secondly, to propose a novel method of
fuzzy hierarchical clustering, termed \textit{semantic distillation} --
strongly inspired from the theory of quantum measurement --, we developed to
analyse raw data coming from various types of experiments on DNA arrays. We
illustrate the method by analysing DNA arrays experiments and clustering the
genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence,
Springer-Verla
Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering
The article introduces a new method for applying Quantum Clustering to graph
structures. Quantum Clustering (QC) is a novel density-based unsupervised
learning method that determines cluster centers by constructing a potential
function. In this method, we use the Graph Gradient Descent algorithm to find
the centers of clusters. GPU parallelization is utilized for computing
potential values. We also conducted experiments on five widely used datasets
and evaluated using four indicators. The results show superior performance of
the method. Finally, we discuss the influence of on the experimental
results
Quantum Motif Clustering
We present three quantum algorithms for clustering graphs based on
higher-order patterns, known as motif clustering. One uses a straightforward
application of Grover search, the other two make use of quantum approximate
counting, and all of them obtain square-root like speedups over the fastest
classical algorithms in various settings. In order to use approximate counting
in the context of clustering, we show that for general weighted graphs the
performance of spectral clustering is mostly left unchanged by the presence of
constant (relative) errors on the edge weights. Finally, we extend the original
analysis of motif clustering in order to better understand the role of multiple
`anchor nodes' in motifs and the types of relationships that this method of
clustering can and cannot capture.Comment: 51 pages, 11 figure
Microscopic approach to the spectator matter fragmentation from 400 to 1000 AMeV
A study of multifragmentation of gold nuclei is reported at incident energies
of 400, 600 and 1000 MeV/nucleon using microscopic theory. The present
calculations are done within the framework of quantum molecular dynamics (QMD)
model. The clusterization is performed with advanced sophisticated algorithm
namely \emph{simulated annealing clusterization algorithm} (SACA) along with
conventional spatial correlation method. A quantitative comparison of mean
multiplicity of intermediate mass fragments with experimental findings of
ALADiN group gives excellent agreement showing the ability of SACA method to
reproduce the fragment yields. It also emphasizes the importance of clustering
criterion in describing the fragmentation process within semi-classical model
Variational Quantum and Quantum-Inspired Clustering
Here we present a quantum algorithm for clustering data based on a
variational quantum circuit. The algorithm allows to classify data into many
clusters, and can easily be implemented in few-qubit Noisy Intermediate-Scale
Quantum (NISQ) devices. The idea of the algorithm relies on reducing the
clustering problem to an optimization, and then solving it via a Variational
Quantum Eigensolver (VQE) combined with non-orthogonal qubit states. In
practice, the method uses maximally-orthogonal states of the target Hilbert
space instead of the usual computational basis, allowing for a large number of
clusters to be considered even with few qubits. We benchmark the algorithm with
numerical simulations using real datasets, showing excellent performance even
with one single qubit. Moreover, a tensor network simulation of the algorithm
implements, by construction, a quantum-inspired clustering algorithm that can
run on current classical hardware.Comment: 5 pages, 3 figures, revised versio
- …