The aim of this work is to give an introduction for a non-practical reader to the growing field
of quantum machine learning, which is a recent discipline that combines the research areas …

Recurrent neural networks are the foundation of many sequence-to-sequence models in
machine learning, such as machine translation and speech synthesis. With applied quantum …

Motivated by the parton picture of high-energy quantum chromodynamics, we develop a
single-particle digitization strategy for the efficient quantum simulation of relativistic …

One limitation of the variational quantum eigensolver algorithm is the large number of
measurement steps required to estimate different terms in the Hamiltonian of interest …

In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing
debate regarding its effects on traditional financial markets, particularly the US dollar. This …

Modeling nonlinear activation functions on quantum computers is vital for quantum neurons
employed in fully quantum neural networks, however, remains a challenging task. We …

Artificial neural networks are becoming an integral part of digital solutions to complex
problems. However, employing neural networks on quantum processors faces challenges …

In this work, we aim to confirm the impact of the Fourier series on the quantum machine
learning model. We will propose models, tests, and demonstrations to achieve this objective …

A properly performing and efficient bond market is widely considered important for the
smooth functioning of trading systems in general. An important feature of the bond market for …

The frequency of armed conflicts increased during the last 20 years. The problems of the
emergence of military disputes, not only concern social parameters, but also economic and …