Background: In recent years, the implementation of nuclear physics models in quantum computers has emerged as a promising ...

Digital-analog quantum computing (DAQC) combines digital gates with analog operations, offering an alternative paradigm ...

Sampling from a quantum distribution can be exponentially hard for classical computers and yet could be performed efficiently ...

In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in ...

Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with ...

We propose a high-quality deterministic single-photon source which can not only works in the ultrastrong light-matter ...

Quantum machine learning (QML) is a discipline that holds the promise ofrevolutionizing data processing and problem-solving. ...

A través de la introducción a dos campos de estudio de actualidad, como lo son la computación cuántica y la inteligencia ...

Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known ...

A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative ...

This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations ...

The response of dissipative systems to multi-chromatic fields exhibits generic properties which follow from the discrete ...

We report on the first proof-of-concept system demonstrating how one can control a qubit with mental activity. We developed ...

This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together ...

We propose an adaptive random quantum algorithm to obtain an optimized eigensolver. Specifically, we introduce a general ...

We present an analog version of the quantum approximate optimization algorithm suitable for current quantum annealers. The ...

A digital quantum simulation of the Agassi model from nuclear physics is proposed and analyzed. The proposal is worked out ...

We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integratingknowledge and methods from ...

Over the last few decades, quantum simulations turns out to be a quite suitable solution to the obstacles found when trying ...

A digital quantum simulation for the extended Agassi model is proposed using a quantum platform with eight trapped ions. ...

Mechatronics systems, a macroscopic domain, aim at producing highly efficient engineering platforms, with applications in ...

Pricing interest-rate financial derivatives is a major problem in finance, in which it is crucial to accurately reproduce ...

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly ...

The logistic network design is an abstract optimization problem that, under the assumption of minimal cost, seeks the ...

Systems that can be effectively described as a localized spin-s particle subject to time-dependent fields have attracted ...

Quantum machine learning has emerged as a promising paradigm that could accelerate machine learning calculations. Inside ...

General solutions to the quantum Rabi model involve subspaces with an unbounded number of photons. However, for the ...

Light-matter interactions are an established field that is experiencing a renaissance in recent years due to the introduction ...

Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on ...

Quantum computers will allow calculations beyond existing classical computers. However, current technology is still too ...

In this work, we aim to give an introduction to quantum technologies and quantum computation, providing an overview of its ...

Digital quantum computing paradigm offers highly desirable features such as universality, scalability, and quantum error ...

Preparing the eigenstate, especially the ground state, of a complex Hamiltonian is of great importance in quantum simulations. ...

We present an experimental realisation of a measurement-based adaptation protocol with quantum reinforcement learning in ...

We demonstrate the emergence of selective k-photon interactions in the strong and ultrastrong coupling regimes of the ...

As the building block in symmetric cryptography, designing Boolean functions satisfying multiple properties is an important ...

The characterization of an operator by its eigenvectors and eigenvalues allows us to know its action over any quantum ...

The application in cryptography of quantum algorithms for prime factorization fostered the interest in quantum computing. ...

Solving linear differential equations (LDEs) is a hard problem for classical computers, while quantum algorithms have been ...