Classification is one of the main applications of supervised learning. Recent advancements
in developing quantum computers have opened a new possibility for machine learning on …

Quantum neural networks hold significant promise for numerous applications, particularly as
they can be executed on the current generation of quantum hardware. However, due to …

For a large number of tasks, quantum computing demonstrates the potential for exponential
acceleration over classical computing. In the NISQ era, variable-component subcircuits …

The quantum asymptotically universal multi-feature (QAUM) encoding architecture was
recently introduced and showed improved expressivity and performance in classifying …

In this era of incessant advancements in quantum computing, bridging the gap between
quantum algorithms' hardware requisites and available devices has become crucial. A prime …

Quantum machine learning, an emerging field in the noisy intermediate-scale quantum
(NISQ) era, faces significant challenges in error mitigation during training and inference …