Quantum computers promise to enhance machine learning for practical applications.
Quantum machine learning for real-world data has to handle extensive amounts of high …

Classical machine learning has succeeded in the prediction of both classical and quantum
phases of matter. Notably, kernel methods stand out for their ability to provide interpretable …

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 two-level systems, ie, qubits, form the basis for most quantum machine learning
approaches that have been proposed throughout the years. However, higher dimensional …

In this mini-review, we explore an alternative paradigm to cold dark matter (CDM) postulated
in Λ CDM model. The alternative known as the self-interacting dark matter (SIDM) addresses …

We report unambiguously experimental evidence of a strong orbital current in Nb films with
weak spin-orbit coupling via the spin-torque ferromagnetic resonance spectrum for Fe/Nb …

In a single qubit system, a universal quantum classifier can be realized using the data
reuploading technique. In this study, we propose a new quantum classifier applying this …