The existing body of research on quantum embedding techniques is not only confined in
scope but also lacks a comprehensive understanding of the intricacies of the quantum …

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine
learning (ML) architectures for handling multidimensional data by maintaining data locality …

The convolution operation plays a vital role in a wide range of critical algorithms across
various domains, such as digital image processing, convolutional neural networks, and …

Convolution is a fundamental operation in many critical algorithms across various domains,
such as digital image processing, convolutional neural networks, and quantum machine …

Quantum computing (QC) has opened the door to advancements in machine learning (ML)
tasks that are currently implemented in the classical domain. Convolutional neural networks …

Different kinds of wave packet transforms are widely used for extracting multi-scale
structures in signal processing tasks. This paper introduces the quantum circuit …

With the rapid advancement of quantum computing technology, there is a strong motivation
to explore suitable practical applications for quantum algorithms and quantum computers …

In classical machine learning, the convolution operation is leveraged in the eponymous
class of convolutional neural networks (CNNs) for capturing the spatial and/or temporal …

• Data for 1x1 PE was extrapolated for n> 8• Simulator SDK struggled to handle qubit count
larger than 8• Throughput stayed constant and close to 98MB per second• At 12 qubits …