Results 211 to 220 of about 348,558 (295)
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
The use of QML in the realm of nuclear physics at low energy is almost nonexistent. Three examples of the use of quantum computing and quantum machine in nuclear physics are presented: the determination of the phase/shape in nuclear models, the calculation of the ground state energy, and the identification of particles in nuclear physics experiments ...
José‐Enrique García‐Ramos+4 more
wiley +1 more source
Phytosynthesis and Characterization of Silver Nanoparticles from <i>Antigonon leptopus</i>: Assessment of Antibacterial and Cytotoxic Properties. [PDF]
Gastelum-Cabrera M+11 more
europepmc +1 more source
Machine Learning for Maximizing the Memristivity of Single and Coupled Quantum Memristors
Machine learning (ML) methods are proposed to characterize the memristive properties of single and coupled quantum memristors. It is shown that maximizing the memristivity leads to large values in the degree of entanglement of two quantum memristors, unveiling the close relationship between quantum correlations and memory.
Carlos Hernani‐Morales+5 more
wiley +1 more source
Spatial, Temporal, and Dynamic Behavior of Different Entropies in Seismic Activity: The February 2023 Earthquakes in Türkiye and Syria. [PDF]
Pastén D+3 more
europepmc +1 more source
NÃvel nutricional e de atividade fÃsica em estudantes da rede pública e particular de ensino
Morais, Yara Campanelli de+2 more
openaire +1 more source
Reproducing the Effects of Quantum Deformation in the Undeformed Jaynes‐Cummings Model
The inverse problem approach, where atomic probabilities are modulated according to a time‐dependent coupling, is studied for the Jaynes‐Cummings (JC) model. In particular, emphasis is placed on how to reproduce the effects of quantum deformation in a non‐deformed JC model.
Thiago T. Tsutsui+2 more
wiley +1 more source