Quantum Machine Learning (QML) is a branch of quantum computing that combines classical machine learning with the principles of quantum mechanics. It is emerging as an alternative to classical machine learning which exploits the quantum mechanical properties of entanglement and superposition to express the hidden patterns in the data. This reduces computational resources also the time required for processing. This research work is a comparative study, which compares the overall performance of Classical multi-class Support Vector Classifiers (SVC) with Quantum multi-class Support Vector Classifiers (QSVC). In this work, we used benchmark Hyperspectral Remotely Sensed datasets namely, Pavia University and Salinas-A on IBM gate-based Quantum Computer(QC). Here, in QSVC, kernel is generated by QC, and Qiskit’s Support Vector Classifier is used for classification. Classification of the pixels into their respective classes was experimented using two techniques, One vs One (OVO) and One v/s Rest (OVR). Quantum kernels are very expressive when compared to their classical counterparts and can learn complex data more efficiently. The overall accuracy of classification by QSVC is comparable to that of the classical SVC. We summarize our research by saying that QSVC performs better than SVC.
A worldwide collaboration attempts to confirm the existence of gravitational waves predicted by Einstein's theory
of General Relativity, through direct observation with a network of large-scale laser interferometric antennas.
This paper is a contribution to the methodologies used to scrutinize the data in order to reveal the tiny signature
of a gravitational wave from rare cataclysmic events of astrophysical origin. More specifically, we are interested
in the detection of short frequency modulated transients or gravitational wave chirps. The amount of information
about the frequency vs. time evolution is limited: we only know that it is smooth. The detection problem is
thus non-parametric. We introduce a finite family of "template waveforms" which accurately samples the set of
admissible chirps. The templates are constructed as a puzzle, by assembling elementary bricks (the chirplets)
taken a dictionary. The detection amounts to testing the correlation between the data and the template family.
With an adequate time-frequency mapping, we establish a connection between this correlation measurement and
combinatorial optimization problems of graph theory, from which we obtain efficient algorithms to perform the
calculation. We present two variants. A first one addresses the case of amplitude modulated chirps and the
second allows the joint analysis of the data from several antennas. Those methods are not limited to the specific
context for which they have been developed. We pay a particular attention to the aspects that can be source of
inspiration for other applications.
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