4,780 research outputs found
Statistical Geometry in Quantum Mechanics
A statistical model M is a family of probability distributions, characterised
by a set of continuous parameters known as the parameter space. This possesses
natural geometrical properties induced by the embedding of the family of
probability distributions into the Hilbert space H. By consideration of the
square-root density function we can regard M as a submanifold of the unit
sphere in H. Therefore, H embodies the `state space' of the probability
distributions, and the geometry of M can be described in terms of the embedding
of in H. The geometry in question is characterised by a natural Riemannian
metric (the Fisher-Rao metric), thus allowing us to formulate the principles of
classical statistical inference in a natural geometric setting. In particular,
we focus attention on the variance lower bounds for statistical estimation, and
establish generalisations of the classical Cramer-Rao and Bhattacharyya
inequalities. The statistical model M is then specialised to the case of a
submanifold of the state space of a quantum mechanical system. This is pursued
by introducing a compatible complex structure on the underlying real Hilbert
space, which allows the operations of ordinary quantum mechanics to be
reinterpreted in the language of real Hilbert space geometry. The application
of generalised variance bounds in the case of quantum statistical estimation
leads to a set of higher order corrections to the Heisenberg uncertainty
relations for canonically conjugate observables.Comment: 32 pages, LaTex file, Extended version to include quantum measurement
theor
Quantum Chi-Squared and Goodness of Fit Testing
The density matrix in quantum mechanics parameterizes the statistical
properties of the system under observation, just like a classical probability
distribution does for classical systems. The expectation value of observables
cannot be measured directly, it can only be approximated by applying classical
statistical methods to the frequencies by which certain measurement outcomes
(clicks) are obtained. In this paper, we make a detailed study of the
statistical fluctuations obtained during an experiment in which a hypothesis is
tested, i.e. the hypothesis that a certain setup produces a given quantum
state. Although the classical and quantum problem are very much related to each
other, the quantum problem is much richer due to the additional optimization
over the measurement basis. Just as in the case of classical hypothesis
testing, the confidence in quantum hypothesis testing scales exponentially in
the number of copies. In this paper, we will argue 1) that the physically
relevant data of quantum experiments is only contained in the frequencies of
the measurement outcomes, and that the statistical fluctuations of the
experiment are essential, so that the correct formulation of the conclusions of
a quantum experiment should be given in terms of hypothesis tests, 2) that the
(classical) test for distinguishing two quantum states gives rise to
the quantum divergence when optimized over the measurement basis, 3)
present a max-min characterization for the optimal measurement basis for
quantum goodness of fit testing, find the quantum measurement which leads both
to the maximal Pitman and Bahadur efficiency, and determine the associated
divergence rates.Comment: 22 Pages, with a new section on parameter estimatio
The SIC Question: History and State of Play
Recent years have seen significant advances in the study of symmetric
informationally complete (SIC) quantum measurements, also known as maximal sets
of complex equiangular lines. Previously, the published record contained
solutions up to dimension 67, and was with high confidence complete up through
dimension 50. Computer calculations have now furnished solutions in all
dimensions up to 151, and in several cases beyond that, as large as dimension
844. These new solutions exhibit an additional type of symmetry beyond the
basic definition of a SIC, and so verify a conjecture of Zauner in many new
cases. The solutions in dimensions 68 through 121 were obtained by Andrew
Scott, and his catalogue of distinct solutions is, with high confidence,
complete up to dimension 90. Additional results in dimensions 122 through 151
were calculated by the authors using Scott's code. We recap the history of the
problem, outline how the numerical searches were done, and pose some
conjectures on how the search technique could be improved. In order to
facilitate communication across disciplinary boundaries, we also present a
comprehensive bibliography of SIC research.Comment: 16 pages, 1 figure, many references; v3: updating bibliography,
dimension eight hundred forty fou
Estimating the quadratic covariation matrix from noisy observations: Local method of moments and efficiency
An efficient estimator is constructed for the quadratic covariation or
integrated co-volatility matrix of a multivariate continuous martingale based
on noisy and nonsynchronous observations under high-frequency asymptotics. Our
approach relies on an asymptotically equivalent continuous-time observation
model where a local generalised method of moments in the spectral domain turns
out to be optimal. Asymptotic semi-parametric efficiency is established in the
Cram\'{e}r-Rao sense. Main findings are that nonsynchronicity of observation
times has no impact on the asymptotics and that major efficiency gains are
possible under correlation. Simulations illustrate the finite-sample behaviour.Comment: Published in at http://dx.doi.org/10.1214/14-AOS1224 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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