2,685 research outputs found
Quantum fidelity for one-dimensional Dirac fermions and two-dimensional Kitaev model in the thermodynamic limit
We study the scaling behavior of the fidelity () in the thermodynamic
limit using the examples of a system of Dirac fermions in one dimension and the
Kitaev model on a honeycomb lattice. We show that the thermodynamic fidelity
inside the gapless as well as gapped phases follow power-law scalings, with the
power given by some of the critical exponents of the system. The generic
scaling forms of for an anisotropic quantum critical point for both
thermodynamic and non-thermodynamic limits have been derived and verified for
the Kitaev model. The interesting scaling behavior of inside the gapless
phase of the Kitaev model is also discussed. Finally, we consider a rotation of
each spin in the Kitaev model around the z axis and calculate through the
overlap between the ground states for angle of rotation and
, respectively. We thereby show that the associated geometric phase
vanishes. We have supplemented our analytical calculations with numerical
simulations wherever necessary.Comment: 10 pages, 8 figure
Enhanced precision bound of low-temperature quantum thermometry via dynamical control
High-precision low-temperature thermometry is a challenge for experimental
quantum physics and quantum sensing. Here we consider a thermometer modelled by
a dynamically-controlled multilevel quantum probe in contact with a bath.
Dynamical control in the form of periodic modulation of the energy-level
spacings of the quantum probe can dramatically increase the maximum accuracy
bound of low-temperatures estimation, by maximizing the relevant quantum Fisher
information. As opposed to the diverging relative error bound at low
temperatures in conventional quantum thermometry, periodic modulation of the
probe allows for low-temperature thermometry with temperature-independent
relative error bound. The proposed approach may find diverse applications
related to precise probing of the temperature of many-body quantum systems in
condensed matter and ultracold gases, as well as in different branches of
quantum metrology beyond thermometry, for example in precise probing of
different Hamiltonian parameters in many-body quantum critical systems.Comment: 8 pages, 4 figure
Loschmidt echo with a non-equilibrium initial state: early time scaling and enhanced decoherence
We study the Loschmidt echo (LE) in a central spin model in which a central
spin is globally coupled to an environment (E) which is subjected to a small
and sudden quench at so that its state at , remains the same as
the ground state of the initial environmental Hamiltonian before the quench;
this leads to a non-equilibrium situation. This state now evolves with two
Hamiltonians, the final Hamiltonian following the quench and its modified
version which incorporates an additional term arising due to the coupling of
the central spin to the environment. Using a generic short-time scaling of the
decay rate, we establish that in the early time limit, the rate of decay of the
LE (or the overlap between two states generated from the initial state evolving
through two channels) close to the quantum critical point (QCP) of E is
independent of the quenching. We do also study the temporal evolution of the LE
and establish the presence of a crossover to a situation where the quenching
becomes irrelevant. In the limit of large quench amplitude the non-equilibrium
initial condition is found to result in a drastic increase in decoherence at
large times, even far away from a QCP. These generic results are verified
analytically as well as numerically, choosing E to be a transverse Ising chain
where the transverse field is suddenly quenched.Comment: 5 pages, 6 figures; New results, figures and references added, title
change
Collective effects enhanced multi-qubit information engines
We study a quantum information engine (QIE) modeled by a multi-qubit working
medium (WM) collectively coupled to a single thermal bath. We show that one can
harness the collective effects to significantly enhance the performance of the
QIE, as compared to equivalent engines lacking collective effects. We use one
bit of information about the WM magnetization to extract work from the thermal
bath. We analyze the work output, noise-to-signal ratio and thermodynamic
uncertainty relation, and contrast these performance metrics of a collective
QIE with that of an engine whose WM qubits are coupled independently to a
thermal bath. We show that in the limit of high temperatures of the thermal
bath, a collective QIE always outperforms its independent counterpart. In
contrast to quantum heat engines, where collective enhancement in specific heat
plays a direct role in improving the performance of the engines, here the
collective advantage stems from higher occupation probabilities for the higher
energy levels of the positive magnetization states, as compared to the
independent case.Comment: 9 pages, 7 figure
Efficiency of quantum controlled non-Markovian thermalization
We study optimal control strategies to optimize the relaxation rate towards
the fixed point of a quantum system in the presence of a non-Markovian
dissipative bath. Contrary to naive expectations that suggest that memory
effects might be exploited to improve optimal control effectiveness,
non-Markovian effects influence the optimal strategy in a non trivial way: we
present a necessary condition to be satisfied so that the effectiveness of
optimal control is enhanced by non-Markovianity subject to suitable unitary
controls. For illustration, we specialize our findings for the case of the
dynamics of single qubit amplitude damping channels. The optimal control
strategy presented here can be used to implement optimal cooling processes in
quantum technologies and may have implications in quantum thermodynamics when
assessing the efficiency of thermal micro-machines.Comment: 7 pages, 3 figure
A Simple Flood Forecasting Scheme Using Wireless Sensor Networks
This paper presents a forecasting model designed using WSNs (Wireless Sensor
Networks) to predict flood in rivers using simple and fast calculations to
provide real-time results and save the lives of people who may be affected by
the flood. Our prediction model uses multiple variable robust linear regression
which is easy to understand and simple and cost effective in implementation, is
speed efficient, but has low resource utilization and yet provides real time
predictions with reliable accuracy, thus having features which are desirable in
any real world algorithm. Our prediction model is independent of the number of
parameters, i.e. any number of parameters may be added or removed based on the
on-site requirements. When the water level rises, we represent it using a
polynomial whose nature is used to determine if the water level may exceed the
flood line in the near future. We compare our work with a contemporary
algorithm to demonstrate our improvements over it. Then we present our
simulation results for the predicted water level compared to the actual water
level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc,
Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple
Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201
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