Constant-sized robust self-tests for states and measurements of
unbounded dimension
- URL: http://arxiv.org/abs/2103.01729v1
- Date: Tue, 2 Mar 2021 14:02:17 GMT
- Title: Constant-sized robust self-tests for states and measurements of
unbounded dimension
- Authors: Laura Man\v{c}inska, Jitendra Prakash, Christopher Schafhauser
- Abstract summary: We show that correlations $p_n,x$ robustly self-test the underlying states and measurements.
We are the first to exhibit a constant-sized self-test for measurements of unbounded dimension.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider correlations, $p_{n,x}$, arising from measuring a maximally
entangled state using $n$ measurements with two outcomes each, constructed from
$n$ projections that add up to $xI$. We show that the correlations $p_{n,x}$
robustly self-test the underlying states and measurements. To achieve this, we
lift the group-theoretic Gowers-Hatami based approach for proving robust
self-tests to a more natural algebraic framework. A key step is to obtain an
analogue of the Gowers-Hatami theorem allowing to perturb an "approximate"
representation of the relevant algebra to an exact one.
For $n=4$, the correlations $p_{n,x}$ self-test the maximally entangled state
of every odd dimension as well as 2-outcome projective measurements of
arbitrarily high rank. The only other family of constant-sized self-tests for
strategies of unbounded dimension is due to Fu (QIP 2020) who presents such
self-tests for an infinite family of maximally entangled states with even local
dimension. Therefore, we are the first to exhibit a constant-sized self-test
for measurements of unbounded dimension as well as all maximally entangled
states with odd local dimension.
Related papers
- Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Constant-sized self-tests for maximally entangled states and single projective measurements [0.0]
Self-testing is a powerful certification of quantum systems relying on measured, classical statistics.
This paper considers self-testing in bipartite Bell scenarios with small number of inputs and outputs, but with quantum states and measurements of arbitrarily large dimension.
arXiv Detail & Related papers (2023-06-23T13:43:56Z) - Self-testing in prepare-and-measure scenarios and a robust version of
Wigner's theorem [0.0]
We consider communication scenarios where one party sends quantum states of known dimensionality $D$, prepared with an untrusted apparatus, to another, distant party.
We prove that, for any ensemble of reference pure quantum states, there exists one such prepare-and-measure scenario and a linear functional $W$ on its observed measurement probabilities.
arXiv Detail & Related papers (2023-06-01T14:27:24Z) - Pseudonorm Approachability and Applications to Regret Minimization [73.54127663296906]
We convert high-dimensional $ell_infty$-approachability problems to low-dimensional pseudonorm approachability problems.
We develop an algorithmic theory of pseudonorm approachability, analogous to previous work on approachability for $ell$ and other norms.
arXiv Detail & Related papers (2023-02-03T03:19:14Z) - Mutually unbiased frames [0.0]
The concept of mutually unbiased frames is introduced as the most general notion of unbiasedness for sets composed by linearly independent and normalized vectors.
It encompasses the already existing notions of unbiasedness for orthonormal bases, regular simplices, equiangular tight frames, positive operator valued measure, and also includes symmetric informationally complete quantum measurements.
arXiv Detail & Related papers (2021-10-15T18:04:20Z) - Spatially relaxed inference on high-dimensional linear models [48.989769153211995]
We study the properties of ensembled clustered inference algorithms which combine spatially constrained clustering, statistical inference, and ensembling to aggregate several clustered inference solutions.
We show that ensembled clustered inference algorithms control the $delta$-FWER under standard assumptions for $delta$ equal to the largest cluster diameter.
arXiv Detail & Related papers (2021-06-04T16:37:19Z) - Stochastic behavior of outcome of Schur-Weyl duality measurement [45.41082277680607]
We focus on the measurement defined by the decomposition based on Schur-Weyl duality on $n$ qubits.
We derive various types of distribution including a kind of central limit when $n$ goes to infinity.
arXiv Detail & Related papers (2021-04-26T15:03:08Z) - Dimension-agnostic inference using cross U-statistics [33.17951971728784]
We introduce an approach that uses variational representations of existing test statistics along with sample splitting and self-normalization.
The resulting statistic can be viewed as a careful modification of degenerate U-statistics, dropping diagonal blocks and retaining off-diagonal blocks.
arXiv Detail & Related papers (2020-11-10T12:21:34Z) - The Generalized Lasso with Nonlinear Observations and Generative Priors [63.541900026673055]
We make the assumption of sub-Gaussian measurements, which is satisfied by a wide range of measurement models.
We show that our result can be extended to the uniform recovery guarantee under the assumption of a so-called local embedding property.
arXiv Detail & Related papers (2020-06-22T16:43:35Z) - A Concentration of Measure and Random Matrix Approach to Large
Dimensional Robust Statistics [45.24358490877106]
This article studies the emphrobust covariance matrix estimation of a data collection $X = (x_1,ldots,x_n)$ with $x_i = sqrt tau_i z_i + m$.
We exploit this semi-metric along with concentration of measure arguments to prove the existence and uniqueness of the robust estimator as well as evaluate its limiting spectral distribution.
arXiv Detail & Related papers (2020-06-17T09:02:26Z) - Optimal rates for independence testing via $U$-statistic permutation
tests [7.090165638014331]
We study the problem of independence testing given independent and identically distributed pairs taking values in a $sigma$-finite, separable measure space.
We first show that there is no valid test of independence that is uniformly consistent against alternatives of the form $f: D(f) geq rho2 $.
arXiv Detail & Related papers (2020-01-15T19:04:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.