33,804 research outputs found

### Group theoretical study of LOCC-detection of maximally entangled state using hypothesis testing

In the asymptotic setting, the optimal test for hypotheses testing of the
maximally entangled state is derived under several locality conditions for
measurements. The optimal test is obtained in several cases with the asymptotic
framework as well as the finite-sample framework. In addition, the experimental
scheme for the optimal test is presented

### Unitary-process discrimination with error margin

We investigate a discrimination scheme between unitary processes. By
introducing a margin for the probability of erroneous guess, this scheme
interpolates the two standard discrimination schemes: minimum-error and
unambiguous discrimination. We present solutions for two cases. One is the case
of two unitary processes with general prior probabilities. The other is the
case with a group symmetry: the processes comprise a projective representation
of a finite group. In the latter case, we found that unambiguous discrimination
is a kind of "all or nothing": the maximum success probability is either 0 or
1. We also closely analyze how entanglement with an auxiliary system improves
discrimination performance.Comment: 9 pages, 3 figures, presentation improved, typos corrected, final
versio

### Optimal estimation of a physical observable's expectation value for pure states

We study the optimal way to estimate the quantum expectation value of a
physical observable when a finite number of copies of a quantum pure state are
presented. The optimal estimation is determined by minimizing the squared error
averaged over all pure states distributed in a unitary invariant way. We find
that the optimal estimation is "biased", though the optimal measurement is
given by successive projective measurements of the observable. The optimal
estimate is not the sample average of observed data, but the arithmetic average
of observed and "default nonobserved" data, with the latter consisting of all
eigenvalues of the observable.Comment: v2: 5pages, typos corrected, journal versio

### Two quantum analogues of Fisher information from a large deviation viewpoint of quantum estimation

We discuss two quantum analogues of Fisher information, symmetric logarithmic
derivative (SLD) Fisher information and Kubo-Mori-Bogoljubov (KMB) Fisher
information from a large deviation viewpoint of quantum estimation and prove
that the former gives the true bound and the latter gives the bound of
consistent superefficient estimators. In another comparison, it is shown that
the difference between them is characterized by the change of the order of
limits.Comment: LaTeX with iopart.cls, iopart12.clo, iopams.st

### Curvature and topological effects on dynamical symmetry breaking in a four- and eight-fermion interaction model

A dynamical mechanism for symmetry breaking is investigated under the
circumstances with the finite curvature, finite size and non-trivial topology.
A four- and eight-fermion interaction model is considered as a prototype model
which induces symmetry breaking at GUT era. Evaluating the effective potential
in the leading order of the 1/N-expansion by using the dimensional
regularization, we explicitly calculate the phase boundary which divides the
symmetric and the broken phase in a weakly curved space-time and a flat
space-time with non-trivial topology, $R^{D-1} \otimes S^1$.Comment: 20 pages, 21 figure

### Determining Structurally Identifiable Parameter Combinations Using Subset Profiling

Identifiability is a necessary condition for successful parameter estimation
of dynamic system models. A major component of identifiability analysis is
determining the identifiable parameter combinations, the functional forms for
the dependencies between unidentifiable parameters. Identifiable combinations
can help in model reparameterization and also in determining which parameters
may be experimentally measured to recover model identifiability. Several
numerical approaches to determining identifiability of differential equation
models have been developed, however the question of determining identifiable
combinations remains incompletely addressed. In this paper, we present a new
approach which uses parameter subset selection methods based on the Fisher
Information Matrix, together with the profile likelihood, to effectively
estimate identifiable combinations. We demonstrate this approach on several
example models in pharmacokinetics, cellular biology, and physiology

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