6 research outputs found
On asymptotic continuity of functions of quantum states
A useful kind of continuity of quantum states functions in asymptotic regime
is so-called asymptotic continuity. In this paper we provide general tools for
checking if a function possesses this property. First we prove equivalence of
asymptotic continuity with so-called it robustness under admixture. This allows
us to show that relative entropy distance from a convex set including maximally
mixed state is asymptotically continuous. Subsequently, we consider it arrowing
- a way of building a new function out of a given one. The procedure originates
from constructions of intrinsic information and entanglement of formation. We
show that arrowing preserves asymptotic continuity for a class of functions
(so-called subextensive ones). The result is illustrated by means of several
examples.Comment: Minor corrections, version submitted for publicatio
Rank-based model selection for multiple ions quantum tomography
The statistical analysis of measurement data has become a key component of
many quantum engineering experiments. As standard full state tomography becomes
unfeasible for large dimensional quantum systems, one needs to exploit prior
information and the "sparsity" properties of the experimental state in order to
reduce the dimensionality of the estimation problem. In this paper we propose
model selection as a general principle for finding the simplest, or most
parsimonious explanation of the data, by fitting different models and choosing
the estimator with the best trade-off between likelihood fit and model
complexity. We apply two well established model selection methods -- the Akaike
information criterion (AIC) and the Bayesian information criterion (BIC) -- to
models consising of states of fixed rank and datasets such as are currently
produced in multiple ions experiments. We test the performance of AIC and BIC
on randomly chosen low rank states of 4 ions, and study the dependence of the
selected rank with the number of measurement repetitions for one ion states. We
then apply the methods to real data from a 4 ions experiment aimed at creating
a Smolin state of rank 4. The two methods indicate that the optimal model for
describing the data lies between ranks 6 and 9, and the Pearson test
is applied to validate this conclusion. Additionally we find that the mean
square error of the maximum likelihood estimator for pure states is close to
that of the optimal over all possible measurements.Comment: 24 pages, 6 figures, 3 table
Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
Intuitively, if a density operator has small rank, then it should be easier
to estimate from experimental data, since in this case only a few eigenvectors
need to be learned. We prove two complementary results that confirm this
intuition. First, we show that a low-rank density matrix can be estimated using
fewer copies of the state, i.e., the sample complexity of tomography decreases
with the rank. Second, we show that unknown low-rank states can be
reconstructed from an incomplete set of measurements, using techniques from
compressed sensing and matrix completion. These techniques use simple Pauli
measurements, and their output can be certified without making any assumptions
about the unknown state.
We give a new theoretical analysis of compressed tomography, based on the
restricted isometry property (RIP) for low-rank matrices. Using these tools, we
obtain near-optimal error bounds, for the realistic situation where the data
contains noise due to finite statistics, and the density matrix is full-rank
with decaying eigenvalues. We also obtain upper-bounds on the sample complexity
of compressed tomography, and almost-matching lower bounds on the sample
complexity of any procedure using adaptive sequences of Pauli measurements.
Using numerical simulations, we compare the performance of two compressed
sensing estimators with standard maximum-likelihood estimation (MLE). We find
that, given comparable experimental resources, the compressed sensing
estimators consistently produce higher-fidelity state reconstructions than MLE.
In addition, the use of an incomplete set of measurements leads to faster
classical processing with no loss of accuracy.
Finally, we show how to certify the accuracy of a low rank estimate using
direct fidelity estimation and we describe a method for compressed quantum
process tomography that works for processes with small Kraus rank.Comment: 16 pages, 3 figures. Matlab code included with the source file
Quantum Tasks in Minkowski Space
The fundamental properties of quantum information and its applications to
computing and cryptography have been greatly illuminated by considering
information-theoretic tasks that are provably possible or impossible within
non-relativistic quantum mechanics. I describe here a general framework for
defining tasks within (special) relativistic quantum theory and illustrate it
with examples from relativistic quantum cryptography and relativistic
distributed quantum computation. The framework gives a unified description of
all tasks previously considered and also defines a large class of new questions
about the properties of quantum information in relation to Minkowski causality.
It offers a way of exploring interesting new fundamental tasks and
applications, and also highlights the scope for a more systematic understanding
of the fundamental information-theoretic properties of relativistic quantum
theory