Detecting continuous variable entanglement in phase space with the
$Q$-distribution
- URL: http://arxiv.org/abs/2211.17165v2
- Date: Mon, 15 Jan 2024 14:58:46 GMT
- Title: Detecting continuous variable entanglement in phase space with the
$Q$-distribution
- Authors: Martin G\"arttner and Tobias Haas and Johannes Noll
- Abstract summary: We prove a class of continuous variable entanglement criteria based on the Husimi $Q$-distribution.
We discuss their generality, which roots in the possibility to optimize over the set of concave functions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We prove a general class of continuous variable entanglement criteria based
on the Husimi $Q$-distribution, which represents a quantum state in canonical
phase space, by employing a theorem by Lieb and Solovej. We discuss their
generality, which roots in the possibility to optimize over the set of concave
functions, from the perspective of continuous majorization theory and show that
with this approach families of entropic as well as second moment criteria
follow as special cases. All derived criteria are compared to corresponding
marginal based criteria and the strength of the phase space approach is
demonstrated for a family of prototypical example states where only our
criteria flag entanglement. Further, we explore their optimization prospects in
two experimentally relevant scenarios characterized by sparse data: finite
detector resolution and finite statistics. In both scenarios optimization leads
to clear improvements enlarging the class of detected states and the
signal-to-noise ratio of the detection, respectively.
Related papers
- Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis [56.442307356162864]
We study the theoretical aspects of score-based discrete diffusion models under the Continuous Time Markov Chain (CTMC) framework.
We introduce a discrete-time sampling algorithm in the general state space $[S]d$ that utilizes score estimators at predefined time points.
Our convergence analysis employs a Girsanov-based method and establishes key properties of the discrete score function.
arXiv Detail & Related papers (2024-10-03T09:07:13Z) - Relative Representations: Topological and Geometric Perspectives [53.88896255693922]
Relative representations are an established approach to zero-shot model stitching.
We introduce a normalization procedure in the relative transformation, resulting in invariance to non-isotropic rescalings and permutations.
Second, we propose to deploy topological densification when fine-tuning relative representations, a topological regularization loss encouraging clustering within classes.
arXiv Detail & Related papers (2024-09-17T08:09:22Z) - Distributed Markov Chain Monte Carlo Sampling based on the Alternating
Direction Method of Multipliers [143.6249073384419]
In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers.
We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art.
In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
arXiv Detail & Related papers (2024-01-29T02:08:40Z) - Optimizing detection of continuous variable entanglement for limited
data [0.0]
We consider the scenario of coarse grained measurements, or finite detector resolution, where the values of the Husimi $Q$-distribution are only known on a grid of points in phase space.
We customize our entanglement criteria to maximize the statistical significance of the detection for a given finite number of samples.
arXiv Detail & Related papers (2022-11-30T17:05:08Z) - General class of continuous variable entanglement criteria [0.0]
We present a general class of entanglement criteria for continuous variable systems.
Our criteria reveal entanglement of families of states undetected by any commonly used criteria.
arXiv Detail & Related papers (2022-11-30T16:54:39Z) - Computationally Efficient PAC RL in POMDPs with Latent Determinism and
Conditional Embeddings [97.12538243736705]
We study reinforcement learning with function approximation for large-scale Partially Observable Decision Processes (POMDPs)
Our algorithm provably scales to large-scale POMDPs.
arXiv Detail & Related papers (2022-06-24T05:13:35Z) - Optimal variance-reduced stochastic approximation in Banach spaces [114.8734960258221]
We study the problem of estimating the fixed point of a contractive operator defined on a separable Banach space.
We establish non-asymptotic bounds for both the operator defect and the estimation error.
arXiv Detail & Related papers (2022-01-21T02:46:57Z) - Entropic entanglement criteria in phase space [0.0]
We derive entropic inseparability criteria for the phase space representation of quantum states.
Our criteria are based on a joint distribution known as the Husimi Q-distribution.
We show that our criteria certify entanglement in previously undetectable regions.
arXiv Detail & Related papers (2021-06-16T13:46:16Z) - Localisation in quasiperiodic chains: a theory based on convergence of
local propagators [68.8204255655161]
We present a theory of localisation in quasiperiodic chains with nearest-neighbour hoppings, based on the convergence of local propagators.
Analysing the convergence of these continued fractions, localisation or its absence can be determined, yielding in turn the critical points and mobility edges.
Results are exemplified by analysing the theory for three quasiperiodic models covering a range of behaviour.
arXiv Detail & Related papers (2021-02-18T16:19:52Z) - Probing nonclassicality with matrices of phase-space distributions [0.0]
We devise a method to certify nonclassical features via correlations of phase-space distributions.
Our approach complements and extends recent results that were based on Chebyshev's inequality.
arXiv Detail & Related papers (2020-03-24T18:00:02Z)
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.