23,619 research outputs found
Spectral implementation of some quantum algorithms by one- and two-dimensional nuclear magnetic resonance
Quantum information processing has been effectively demonstrated on a small
number of qubits by nuclear magnetic resonance. An important subroutine in any
computing is the readout of the output. ``Spectral implementation'' originally
suggested by Z.L. Madi, R. Bruschweiler and R.R. Ernst,
[J. Chem. Phys. 109, 10603 (1999)], provides an elegant method of readout
with the use of an extra `observer' qubit. At the end of computation, detection
of the observer qubit provides the output via the multiplet structure of its
spectrum. In "spectral implementation" by two-dimensional experiment the
observer qubit retains the memory of input state during computation, thereby
providing correlated information on input and output, in the same spectrum.
"Spectral implementation" of Grover's search algorithm, approximate quantum
counting, a modified version of Berstein-Vazirani problem, and Hogg's algorithm
is demonstrated here in three and four-qubit systems.Comment: 39 pages,11 figure
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
WavePacket: A Matlab package for numerical quantum dynamics. III: Quantum-classical simulations and surface hopping trajectories
WavePacket is an open-source program package for numerical simulations in
quantum dynamics. Building on the previous Part I [Comp. Phys. Comm. 213,
223-234 (2017)] and Part II [Comp. Phys. Comm. 228, 229-244 (2018)] which dealt
with quantum dynamics of closed and open systems, respectively, the present
Part III adds fully classical and mixed quantum-classical propagations to
WavePacket. In those simulations classical phase-space densities are sampled by
trajectories which follow (diabatic or adiabatic) potential energy surfaces. In
the vicinity of (genuine or avoided) intersections of those surfaces
trajectories may switch between surfaces. To model these transitions, two
classes of stochastic algorithms have been implemented: (1) J. C. Tully's
fewest switches surface hopping and (2) Landau-Zener based single switch
surface hopping. The latter one offers the advantage of being based on
adiabatic energy gaps only, thus not requiring non-adiabatic coupling
information any more.
The present work describes the MATLAB version of WavePacket 6.0.2 which is
essentially an object-oriented rewrite of previous versions, allowing to
perform fully classical, quantum-classical and quantum-mechanical simulations
on an equal footing, i.e., for the same physical system described by the same
WavePacket input. The software package is hosted and further developed at the
Sourceforge platform, where also extensive Wiki-documentation as well as
numerous worked-out demonstration examples with animated graphics are
available
- …