Using Green's function formalism, we introduce a non-Hermitian Fermi-Dirac distribution and derive an analytical expression for the persistent current that relies solely on the complex spectrum.
We demonstrate the uniform magnonic mode, which spreads over the entire Yu-Shiba-Rusinov chain, becomes imprinted with the parity of the ground state, and can discriminate between Majorana and trivial zero modes.
We have adapted the capsule network architecture for quantum machine learning, which showcases an improved classification accuracy, demonstrates a quantum speedup, and paves the way to explore explainable quantum AI.
We introduced a Markovian quantum neuroevolution algorithm that autonomously finds near-optimal quantum neural networks for different machine-learning tasks, highlighting the potential of genetic algorithms in quantum architecture search.
We found that inhomogeneous quantum spin chains support spin supercurrents, that enhance the entanglement between spins, and can be detected using dispersive techniques.
We proposed a magnetized, radiatively efficient thin accretion disc model (M-thin) to scrutinize the disc–jet couplings, and further explain the radio/X-ray correlations in the black hole/neutron star X-ray binary.
