The data-embedding process is one of the bottlenecks of quantum machine learning,
potentially negating any quantum speedups. In light of this, more effective data-encoding …

Quantum machine learning has the potential to provide powerful algorithms for artificial
intelligence. The pursuit of quantum advantage in quantum machine learning is an active …

Today's quantum processors composed of fifty or more qubits have allowed us to enter a
computational era where the output results are not easily simulatable on the world's biggest …

Control and learning are key to technological advancement, both in the classical and
quantum domains, yet their interrelationship is insufficiently clear in the literature, especially …

Although quantum computing hardware has evolved significantly in recent years, spurred by
increasing industrial and government interest, the size limitation of current generation …

The quantum extreme reservoir computation (QERC) is a versatile quantum neural network
model that combines the concepts of extreme machine learning with quantum reservoir …

The increased development of quantum computing hardware in recent years has led to
increased interest in its application to various areas. Finding effective ways to apply this …

Hard Attention Mechanisms (HAMs) effectively filter essential information discretely and
significantly boost the performance of machine learning models on large datasets …

Kernel methods are class of Machine Learning (ML) models that have been widely
employed in the literature for Earth Observation (EO) applications. The increasing …

Quantum extreme reservoir computation (QERC) is a versatile quantum neural network
model that combines the concepts of extreme machine learning with quantum reservoir …