51 research outputs found
High critical temperature nodal superconductors as building block for time-reversal invariant topological superconductivity
We study possible applications of high critical temperature nodal
superconductors for the search for Majorana bound states in the DIII class. We
propose a microscopic analysis of the proximity effect induced by d-wave
superconductors on a semiconductor wire with strong spin-orbit coupling. We
characterize the induced superconductivity on the wire employing a numerical
self-consistent tight-binding Bogoliubov-de Gennes approach, and analytical
considerations on the Green's function. The order parameter induced on the
wire, the pair correlation function, and the renormalization of the Fermi
points are analyzed in detail, as well as the topological phase diagram in the
case of weak coupling. We highlight optimal Hamiltonian parameters to access
the nontrivial topological phase which could display time-reversal invariant
Majorana doublets at the boundaries of the wire
May a dissipative environment be beneficial for quantum annealing?
We discuss the quantum annealing of the fully-connected ferromagnetic -spin model in a dissipative environment at low temperature. This model, in
the large limit, encodes in its ground state the solution to the Grover's
problem of searching in unsorted databases. In the framework of the quantum
circuit model, a quantum algorithm is known for this task, providing a
quadratic speed-up with respect to its best classical counterpart. This
improvement is not recovered in adiabatic quantum computation for an isolated
quantum processor. We analyze the same problem in the presence of a
low-temperature reservoir, using a Markovian quantum master equation in
Lindblad form, and we show that a thermal enhancement is achieved in the
presence of a zero temperature environment moderately coupled to the quantum
annealer.Comment: 4 pages, 1 figure, proceeding of IQIS 201
High-accuracy Hamiltonian learning via delocalized quantum state evolutions
In this Letter, we propose a method to learn the unknown Hamiltonian
governing the dynamics of a quantum many-body system, using measurements on a
single time-dependent state. We investigate the error scaling of our
reconstruction with respect to the experiment duration, measuring an
exponential decrease during the equilibration time. We prove that the accuracy
of the learning process is maximised for states that are delocalized in the
Hamiltonian eigenstates, capable of exploring a large sample of the Hilbert
space. Finally, we provide examples of our algorithm on simulated quantum
systems
Dissipative time crystals with long-range Lindbladians
Dissipative time crystals can appear in spin systems, when the symmetry
of the Hamiltonian is broken by the environment, and the square of total spin
operator is conserved. In this manuscript, we relax the latter condition
and show that time-translation-symmetry breaking collective oscillations
persist, in the thermodynamic limit, even in the absence of spin symmetry. We
engineer an ad hoc Lindbladian using power-law decaying spin operators and show
that time-translation symmetry breaking appears when the decay exponent obeys
. This model shows a surprisingly rich phase diagram, including
the time-crystal phase as well as first-order, second-order, and continuous
transitions of the fixed points. We study the phase diagram and the
magnetization dynamics in the mean-field approximation, which we prove to be
exact, in the thermodynamic limit, as the system does not develop sizable
quantum fluctuations, up to the third order cumulant expansion.Comment: 13 pages, 11 figure
Andreev spin-noise detector
We investigate the possibility to employ magnetic Josephson junctions as
magnetic-noise detectors. To illustrate our idea, we consider a system
consisting of a quantum dot coupled to superconducting leads in the presence of
an external magnetic field. Under appropriate assumptions, we relate the noise
in the Josephson current to magnetization noise. At the magnetic field driven
transition the junction sensitivity as magnetic noise detector is
strongly enhanced and it diverges in the zero temperature limit. Moreover, we
demonstrate that, if also dot energy is affected by fluctuations, only the
magnetic noise channel contributes to Josephson current noise response when the
quantum dot is tuned in resonance with superconducting leads.Comment: Review. 14 pages. 3 appendices. 14 figure
Suppression of Kondo-assisted co-tunneling in a spin-1 quantum dot with Spin-Orbit interaction
Kondo-type zero-bias anomalies have been frequently observed in quantum dots
occupied by two electrons and attributed to a spin-triplet configuration that
may become stable under particular circumstances. Conversely, zero-bias
anomalies have been so far quite elusive when quantum dots are occupied by an
even number of electrons greater than two, even though a spin-triplet
configuration is more likely to be stabilized there than for two electrons. We
propose as an origin of this phenomenon the spin-orbit interaction, and we show
how it profoundly alters the conventional Kondo screening scenario in the
simple case of a laterally confined quantum dot with four electrons.Comment: 5 pages, 3 figures, submitted 05May201
Deep learning optimal quantum annealing schedules for random Ising models
A crucial step in the race towards quantum advantage is optimizing quantum
annealing using ad-hoc annealing schedules. Motivated by recent progress in the
field, we propose to employ long short term memory (LSTM) neural networks to
automate the search for optimal annealing schedules for (random) weighted
Max-Cut on regular graphs. By training our network using locally adiabatic
annealing paths, we are able to predict optimal annealing schedules for unseen
instances and even larger graphs than those used for training.Comment: 9 pages, 6 figure
High-accuracy Hamiltonian learning via delocalized quantum state evolutions
Learning the unknown Hamiltonian governing the dynamics of a quantum many-body system is a challenging task. In this manuscript, we propose a possible strategy based on repeated measurements on a single time-dependent state. We prove that the accuracy of the learning process is maximized for states that are delocalized in the Hamiltonian eigenbasis. This implies that delocalization is a quantum resource for Hamiltonian learning, that can be exploited to select optimal initial states for learning algorithms. We investigate the error scaling of our reconstruction with respect to the number of measurements, and we provide examples of our learning algorithm on simulated quantum systems
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