dc.description.abstract | Quantum computing, a paradigm that utilizes the concepts of quantum mechanics, has emerged
as a transformative force in various fields, including machine learning. Within this setting,
Quantum Neural Networks (QNNs) have gained attention for their potential to revolutionize
image classification tasks.
Encoding plays a critical role in bridging the gap between classical data and quantum
computation, allowing QNNs to utilize quantum resources thereby processing and manipulating
classical data in a quantum-mechanical fashion.
In this research paper, we present a study of QNNs applied to image classification using the
well-known MNIST dataset, with a specific focus on investigating the impact of four quantum
encoding methods - Basis, Amplitude, Histogram and Pauli Basis Encoding - on the performance
of these QNNs.
The research revealed that Basis Encoding and Pauli Basis Encoding outperformed Amplitude
Encoding and Histogram Encoding across all the metrics tested. Overally, Pauli Basis Encoding
exhibited the best results in enhancing the accuracy and validation loss of the QNNs | en_US |