Quantum physics has changed the way we understand our environment, and one of its
branches, quantum mechanics, has demonstrated accurate and consistent theoretical …

Variational quantum algorithms have been acknowledged as the leading strategy to realize
near-term quantum advantages in meaningful tasks, including machine learning and …

Classification of quantum data is essential for quantum machine learning and near-term
quantum technologies. In this paper, we propose a new hybrid quantum-classical framework …

Inspired by the success of classical neural networks, there has been tremendous effort to
develop classical effective neural networks into quantum concept. In this paper, a novel …

It is a central fact in quantum mechanics that non-orthogonal states cannot be distinguished
perfectly. In general, the optimal measurement for distinguishing such states is a collective …

Very little is known about the cost landscape for parametrized Quantum Circuits (PQCs).
Nevertheless, PQCs are employed in Quantum Neural Networks and Variational Quantum …

In the problem of quantum channel discrimination, one distinguishes between a given
number of quantum channels, which is done by sending an input state through a channel …

Bosonic variational quantum circuits (VQCs) are crucial for information processing in
microwave cavities, trapped ions, and optical systems, widely applicable in quantum …

A qutrit represents a three-level quantum system, so that one qutrit can encode more
information than a qubit, which corresponds to a two-level quantum system. This work …

A quantum sensor (QS) is able to measure various physical phenomena with extreme
sensitivity. QSs have been used in several applications such as atomic interferometers, but …