Unit:
Department of Informatics and TelecommunicationsΠληροφορική
Author:
CHAMARIAS DIMITRIOS
Supervisors info:
Δημήτριος Συβρίδης, Καθηγητής, Τμήμα Πληροφορικής και Τηλεπικοινωνίων, Εθνικό Καποδιστριακό Πανεπιστήμιο Αθηνών
Αικατερίνη Μανδηλαρά, Δόκτωρ, Τμήμα Πληροφορικής και Τηλεπικοινωνίων, Εθνικό Καποδιστριακό Πανεπιστήμιο Αθηνών
Original Title:
The Effects of Quantum Entanglement on Variational Quantum Classifiers
Translated title:
The Effects of Quantum Entanglement on Variational Quantum Classifiers
Summary:
Variational quantum classifiers (VQCs) are a type of machine learning model that leverage
the principles of quantum mechanics to perform classification tasks. This thesis seeks to
determine if entanglement may be utilized as a freely available resource to improve clas-
sification task performance using VQCs. With the ever-increasing interest in quantum
computing and its potential applications in machine learning, understanding the role of
entanglement in enhancing classification performance becomes imperative. The primary
objective of this research is to explore how the presence of entanglement affects the ac-
curacy and generalization capabilities of variational quantum classifiers. To achieve this,
we employ the concept of global entanglement, which refers to the average entanglement
between multiple qubits within a quantum system. By quantifying the amount of entangle-
ment present in different quantum circuits, we can evaluate its impact on the classifier’s
performance. Finally, we present a case study to demonstrate the effectiveness of the
proposed method. The findings of this research will contribute to the growing body of
knowledge in quantum machine learning and ultimately aid in the development of more
efficient and powerful quantum algorithms for classification tasks.
Main subject category:
Technology - Computer science
Keywords:
quantum circuits, quantum entanglement, machine learning, classific- ation, supervised learning, variational quantum classifier, global entan- glement
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