555 research outputs found
Fisher information and asymptotic normality in system identification for quantum Markov chains
This paper deals with the problem of estimating the coupling constant
of a mixing quantum Markov chain. For a repeated measurement on the
chain's output we show that the outcomes' time average has an asymptotically
normal (Gaussian) distribution, and we give the explicit expressions of its
mean and variance. In particular we obtain a simple estimator of whose
classical Fisher information can be optimized over different choices of
measured observables. We then show that the quantum state of the output
together with the system, is itself asymptotically Gaussian and compute its
quantum Fisher information which sets an absolute bound to the estimation
error. The classical and quantum Fisher informations are compared in a simple
example. In the vicinity of we find that the quantum Fisher
information has a quadratic rather than linear scaling in output size, and
asymptotically the Fisher information is localised in the system, while the
output is independent of the parameter.Comment: 10 pages, 2 figures. final versio
Asymptotically optimal quantum channel reversal for qudit ensembles and multimode Gaussian states
We investigate the problem of optimally reversing the action of an arbitrary
quantum channel C which acts independently on each component of an ensemble of
n identically prepared d-dimensional quantum systems. In the limit of large
ensembles, we construct the optimal reversing channel R* which has to be
applied at the output ensemble state, to retrieve a smaller ensemble of m
systems prepared in the input state, with the highest possible rate m/n. The
solution is found by mapping the problem into the optimal reversal of Gaussian
channels on quantum-classical continuous variable systems, which is here solved
as well. Our general results can be readily applied to improve the
implementation of robust long-distance quantum communication. As an example, we
investigate the optimal reversal rate of phase flip channels acting on a
multi-qubit register.Comment: 17 pages, 3 figure
The singular continuous diffraction measure of the Thue-Morse chain
The paradigm for singular continuous spectra in symbolic dynamics and in
mathematical diffraction is provided by the Thue-Morse chain, in its
realisation as a binary sequence with values in . We revisit this
example and derive a functional equation together with an explicit form of the
corresponding singular continuous diffraction measure, which is related to the
known representation as a Riesz product.Comment: 6 pages, 1 figure; revised and improved versio
Asymptotically optimal purification and dilution of mixed qubit and Gaussian states
Given an ensemble of mixed qubit states, it is possible to increase the
purity of the constituent states using a procedure known as state purification.
The reverse operation, which we refer to as dilution, reduces the level of
purity present in the constituent states. In this paper we find asymptotically
optimal procedures for purification and dilution of an ensemble of i.i.d. mixed
qubit states, for some given input and output purities and an asymptotic output
rate. Our solution involves using the statistical tool of local asymptotic
normality, which recasts the qubit problem in terms of attenuation and
amplification of a single displaced Gaussian state. Therefore, to obtain the
qubit solutions, we must first solve the analogous problems in the Gaussian
setup. We provide full solutions to all of the above, for the (global) trace
norm figure of merit.Comment: 11 pages, 6 figure
Data-driven efficient score tests for deconvolution problems
We consider testing statistical hypotheses about densities of signals in
deconvolution models. A new approach to this problem is proposed. We
constructed score tests for the deconvolution with the known noise density and
efficient score tests for the case of unknown density. The tests are
incorporated with model selection rules to choose reasonable model dimensions
automatically by the data. Consistency of the tests is proved
An objective based classification of aggregation techniques for wireless sensor networks
Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented
Inconsistency of the MLE for the joint distribution of interval censored survival times and continuous marks
This paper considers the nonparametric maximum likelihood estimator (MLE) for
the joint distribution function of an interval censored survival time and a
continuous mark variable. We provide a new explicit formula for the MLE in this
problem. We use this formula and the mark specific cumulative hazard function
of Huang and Louis (1998) to obtain the almost sure limit of the MLE. This
result leads to necessary and sufficient conditions for consistency of the MLE
which imply that the MLE is inconsistent in general. We show that the
inconsistency can be repaired by discretizing the marks. Our theoretical
results are supported by simulations.Comment: 27 pages, 4 figure
Srs2 removes deadly recombination intermediates independently of its interaction with SUMO-modified PCNA
Saccharomyces cerevisiae Srs2 helicase plays at least two distinct functions. One is to prevent recombinational repair through its recruitment by sumoylated Proliferating Cell Nuclear Antigen (PCNA), evidenced in postreplication-repair deficient cells, and a second one is to eliminate potentially lethal intermediates formed by recombination proteins. Both actions are believed to involve the capacity of Srs2 to displace Rad51 upon translocation on single-stranded DNA (ssDNA), though a role of its helicase activity may be important to remove some toxic recombination structures. Here, we described two new mutants, srs2R1 and srs2R3, that have lost the ability to hinder recombinational repair in postreplication-repair mutants, but are still able to remove toxic recombination structures. Although the mutants present very similar phenotypes, the mutated proteins are differently affected in their biochemical activities. Srs2R1 has lost its capacity to interact with sumoylated PCNA while the biochemical activities of Srs2R3 are attenuated (ATPase, helicase, DNA binding and ability to displace Rad51 from ssDNA). In addition, crossover (CO) frequencies are increased in both mutants. The different roles of Srs2, in relation to its eventual recruitment by sumoylated PCNA, are discussed
Bayesian estimation of one-parameter qubit gates
We address estimation of one-parameter unitary gates for qubit systems and
seek for optimal probes and measurements. Single- and two-qubit probes are
analyzed in details focusing on precision and stability of the estimation
procedure. Bayesian inference is employed and compared with the ultimate
quantum limits to precision, taking into account the biased nature of Bayes
estimator in the non asymptotic regime. Besides, through the evaluation of the
asymptotic a posteriori distribution for the gate parameter and the comparison
with the results of Monte Carlo simulated experiments, we show that asymptotic
optimality of Bayes estimator is actually achieved after a limited number of
runs. The robustness of the estimation procedure against fluctuations of the
measurement settings is investigated and the use of entanglement to improve the
overall stability of the estimation scheme is also analyzed in some details.Comment: 10 pages, 5 figure
Quantum learning: optimal classification of qubit states
Pattern recognition is a central topic in Learning Theory with numerous
applications such as voice and text recognition, image analysis, computer
diagnosis. The statistical set-up in classification is the following: we are
given an i.i.d. training set where
represents a feature and is a label attached to that
feature. The underlying joint distribution of is unknown, but we can
learn about it from the training set and we aim at devising low error
classifiers used to predict the label of new incoming features.
Here we solve a quantum analogue of this problem, namely the classification
of two arbitrary unknown qubit states. Given a number of `training' copies from
each of the states, we would like to `learn' about them by performing a
measurement on the training set. The outcome is then used to design mesurements
for the classification of future systems with unknown labels. We find the
asymptotically optimal classification strategy and show that typically, it
performs strictly better than a plug-in strategy based on state estimation.
The figure of merit is the excess risk which is the difference between the
probability of error and the probability of error of the optimal measurement
when the states are known, that is the Helstrom measurement. We show that the
excess risk has rate and compute the exact constant of the rate.Comment: 24 pages, 4 figure
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