Enhancing collective entanglement witnesses through correlation with
state purity
- URL: http://arxiv.org/abs/2312.04957v1
- Date: Fri, 8 Dec 2023 10:39:45 GMT
- Title: Enhancing collective entanglement witnesses through correlation with
state purity
- Authors: Kate\v{r}ina Jir\'akov\'a, Anton\'in \v{C}ernoch, Artur Barasi\'nski
and Karel Lemr
- Abstract summary: This paper analyzes the adverse impact of white noise on collective quantum measurements.
It suggests correlating the outcomes of these measurements with quantum state purity.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper analyzes the adverse impact of white noise on collective quantum
measurements and argues that such noise poses a significant obstacle to the
otherwise straightforward deployment of collective measurements in quantum
communications. The paper then suggests addressing this issue by correlating
the outcomes of these measurements with quantum state purity. To test the
concept, a support vector machine is employed to boost the performance of
several collective entanglement witnesses by incorporating state purity into
the classification task of distinguishing entangled states from separable ones.
Furthermore, the application of machine learning allows to optimize selectivity
of entanglement detection given a target value of sensitivity. A response
operating characteristic curve is reconstructed based on this optimization and
the area under curve calculated to assess the efficacy of the proposed model.
Related papers
- Bounding the Sample Fluctuation for Pure States Certification with Local Random Measurement [4.923287660970805]
Recent advancements in randomized measurement techniques have provided fresh insights in this area.
We investigate the fundamental properties of schemes that certify pure quantum states through random local Haar measurements.
Our results unveil the intrinsic interplay between operator complexity and the efficiency of quantum algorithms, serving as an obstacle to local certification of pure states with long-range entanglement.
arXiv Detail & Related papers (2024-10-22T02:26:44Z) - Bounding conditional entropy of bipartite states with Bell operators [0.0]
We investigate the relationship between CVNE and the violation of Bell inequalities.
By bridging the gap between Bell inequalities and CVNE, our research enhances understanding of the quantum properties of entangled systems.
arXiv Detail & Related papers (2024-10-02T15:15:37Z) - Verification of entangled states under noisy measurements [3.8094794637714027]
Entanglement plays an indispensable role in numerous quantum information and quantum computation tasks.
In recent years, quantum state verification has received increasing attention, yet the challenge of addressing noise effects in implementing this approach remains unsolved.
We provide a systematic assessment of the performance of quantum state verification protocols in the presence of measurement noise.
arXiv Detail & Related papers (2024-06-03T15:59:02Z) - Certified Robustness of Quantum Classifiers against Adversarial Examples
through Quantum Noise [68.1992787416233]
We show that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks.
We derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples.
arXiv Detail & Related papers (2022-11-02T05:17:04Z) - Entanglement quasidistributions for Bell-state measurements [0.0]
We explore the notion of entanglement for detection devices in theory and experiment.
A method is devised that allows one to determine nonlocal quantum coherence of positive operator-valued measures.
We describe the reconstruction of the aforementioned entanglement quasidistributions from raw data and compare the resulting negativities with the expected from theory.
arXiv Detail & Related papers (2022-09-13T18:00:19Z) - Investigation of Different Calibration Methods for Deep Speaker
Embedding based Verification Systems [66.61691401921296]
This paper presents an investigation over several methods of score calibration for deep speaker embedding extractors.
An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system.
arXiv Detail & Related papers (2022-03-28T21:22:22Z) - Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets.
This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets.
The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
arXiv Detail & Related papers (2021-11-15T03:16:56Z) - Deconfounding Scores: Feature Representations for Causal Effect
Estimation with Weak Overlap [140.98628848491146]
We introduce deconfounding scores, which induce better overlap without biasing the target of estimation.
We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data.
In particular, we show that this technique could be an attractive alternative to standard regularizations.
arXiv Detail & Related papers (2021-04-12T18:50:11Z) - Loss Bounds for Approximate Influence-Based Abstraction [81.13024471616417]
Influence-based abstraction aims to gain leverage by modeling local subproblems together with the 'influence' that the rest of the system exerts on them.
This paper investigates the performance of such approaches from a theoretical perspective.
We show that neural networks trained with cross entropy are well suited to learn approximate influence representations.
arXiv Detail & Related papers (2020-11-03T15:33:10Z) - Scope Head for Accurate Localization in Object Detection [135.9979405835606]
We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship.
With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
arXiv Detail & Related papers (2020-05-11T04:00:09Z)
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.