44,435 research outputs found
Quantum Markovian Subsystems: Invariance, Attractivity, and Control
We characterize the dynamical behavior of continuous-time, Markovian quantum
systems with respect to a subsystem of interest. Markovian dynamics describes a
wide class of open quantum systems of relevance to quantum information
processing, subsystem encodings offering a general pathway to faithfully
represent quantum information. We provide explicit linear-algebraic
characterizations of the notion of invariant and noiseless subsystem for
Markovian master equations, under different robustness assumptions for
model-parameter and initial-state variations. The stronger concept of an
attractive quantum subsystem is introduced, and sufficient existence conditions
are identified based on Lyapunov's stability techniques. As a main control
application, we address the potential of output-feedback Markovian control
strategies for quantum pure state-stabilization and noiseless-subspace
generation. In particular, explicit results for the synthesis of stabilizing
semigroups and noiseless subspaces in finite-dimensional Markovian systems are
obtained.Comment: 16 pages, no figures. Revised version with new title, corrected
typos, partial rewriting of Section III.E and some other minor change
Single-bit Feedback and Quantum Dynamical Decoupling
Synthesizing an effective identity evolution in a target system subjected to
unwanted unitary or non-unitary dynamics is a fundamental task for both quantum
control and quantum information processing applications. Here, we investigate
how single-bit, discrete-time feedback capabilities may be exploited to enact
or to enhance quantum procedures for effectively suppressing unwanted dynamics
in a finite-dimensional open quantum system. An explicit characterization of
the joint unitary propagators correctable by a single-bit feedback strategy for
arbitrary evolution time is obtained. For a two-dimensional target system, we
show how by appropriately combining quantum feedback with dynamical decoupling
methods, concatenated feedback-decoupling schemes may be built, which can
operate under relaxed control assumptions and can outperform purely closed-loop
and open-loop protocols.Comment: 12 pages, 2 figure
Generalized Schrieffer-Wolff Formalism for Dissipative Systems
We present a formalized perturbation theory for Markovian open systems in the
language of a generalized Schrieffer-Wolff (SW) transformation. A non-unitary
rotation decouples the unper- turbed steady states from all fast degrees of
freedom, in order to obtain an effective Liouvillian, that reproduces the exact
low excitation spectrum of the system. The transformation is derived in a
constructive way, yielding a perturbative expansion of the effective Liouville
operator. The presented formalism realizes an adiabatic elimination of fast
degrees of freedom to arbitrary orders in the perturbation. We exemplarily
employ the SW formalism to two generic open systems and discuss general
properties of the different orders of the perturbation.Comment: 11 pages, 1 figur
Simultaneous identification of specifically interacting paralogs and inter-protein contacts by Direct-Coupling Analysis
Understanding protein-protein interactions is central to our understanding of
almost all complex biological processes. Computational tools exploiting rapidly
growing genomic databases to characterize protein-protein interactions are
urgently needed. Such methods should connect multiple scales from evolutionary
conserved interactions between families of homologous proteins, over the
identification of specifically interacting proteins in the case of multiple
paralogs inside a species, down to the prediction of residues being in physical
contact across interaction interfaces. Statistical inference methods detecting
residue-residue coevolution have recently triggered considerable progress in
using sequence data for quaternary protein structure prediction; they require,
however, large joint alignments of homologous protein pairs known to interact.
The generation of such alignments is a complex computational task on its own;
application of coevolutionary modeling has in turn been restricted to proteins
without paralogs, or to bacterial systems with the corresponding coding genes
being co-localized in operons. Here we show that the Direct-Coupling Analysis
of residue coevolution can be extended to connect the different scales, and
simultaneously to match interacting paralogs, to identify inter-protein
residue-residue contacts and to discriminate interacting from noninteracting
families in a multiprotein system. Our results extend the potential
applications of coevolutionary analysis far beyond cases treatable so far.Comment: Main Text 19 pages Supp. Inf. 16 page
Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load
We study the compressive diffusion strategies over distributed networks based
on the diffusion implementation and adaptive extraction of the information from
the compressed diffusion data. We demonstrate that one can achieve a comparable
performance with the full information exchange configurations, even if the
diffused information is compressed into a scalar or a single bit. To this end,
we provide a complete performance analysis for the compressive diffusion
strategies. We analyze the transient, steady-state and tracking performance of
the configurations in which the diffused data is compressed into a scalar or a
single-bit. We propose a new adaptive combination method improving the
convergence performance of the compressive diffusion strategies further. In the
new method, we introduce one more freedom-of-dimension in the combination
matrix and adapt it by using the conventional mixture approach in order to
enhance the convergence performance for any possible combination rule used for
the full diffusion configuration. We demonstrate that our theoretical analysis
closely follow the ensemble averaged results in our simulations. We provide
numerical examples showing the improved convergence performance with the new
adaptive combination method.Comment: Submitted to IEEE Transactions on Signal Processin
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