Star network non-n-local correlations can resist consistency noises
better
- URL: http://arxiv.org/abs/2307.09293v2
- Date: Wed, 6 Sep 2023 02:19:23 GMT
- Title: Star network non-n-local correlations can resist consistency noises
better
- Authors: Kan He and Yueran Han
- Abstract summary: We find that star network quantum non-n-local correlations can resist better consistency noises than these in polygon and linear networks.
Polygon and linear network non-n-local correlations can not meet the requirements.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Imperfections from devices can result in the decay or even vanish of
non-n-local correlations as the number of parties n increases in the polygon
and linear quantum networks ([Phys. Rev. A 106, 042206 (2022)] and [Phys. Rev.
A 107, 032404 (2023)]). Even so this phenomenon is also for the special kind of
noises, including consistency noises of a sequence of devices, which means the
sequence of devices have the same probability fails to detect. However, in the
paper, we discover that star network quantum non-n-local correlations can
resist better consistency noises than these in polygon and linear networks. We
first calculate the noisy expected value o f star network non-n-locality and
analyze the persistency conditions theoretically. When assume that congener
devices have the consistency noise, the persistency number of sources n has
been rid of such noises, and approximates to the infinity. Polygon and linear
network non-n-local correlations can not meet the requirements. Furthermore, we
explore the change pattern of the maximal number of sources nmax such that
non-nmax-local correlation can be demonstrated in the star network under the
influence of partially consistent noises, which is more general than consistent
ones.
Related papers
- Network Nonlocality Without Entanglement Of All Sources [0.0]
Entanglement and nonlocality are two important nonclassical features of quantum correlations.
We have analyzed the relation between entanglement content of the sources and detectable non n-locality in two distinct network topologies.
arXiv Detail & Related papers (2024-10-19T15:11:00Z) - Entanglement and operator correlation signatures of many-body quantum Zeno phases in inefficiently monitored noisy systems [49.1574468325115]
The interplay between information-scrambling Hamiltonians and local continuous measurements hosts platforms for exotic measurement-induced phase transition.
We identify a non-monotonic dependence on the local noise strength in both the averaged entanglement and operator correlations.
The analysis of scaling with the system size in a finite length chain indicates that, at finite efficiency, this effect leads to distinct MiPTs for operator correlations and entanglement.
arXiv Detail & Related papers (2024-07-16T13:42:38Z) - Detecting Nontrilocal Correlations In Triangle Networks [0.0]
Correlations in quantum networks with independent sources exhibit a completely novel form of nonclassicality.
A set of criteria is framed in the form of Bell-type inequalities, each of which is necessarily satisfied by trilocal correlations.
measurement on a local product state basis turns out to be sufficient to generate nontrilocal correlations in some quantum networks.
arXiv Detail & Related papers (2023-03-15T16:25:32Z) - Spatial noise correlations beyond nearest-neighbor in ${}^{28}$Si/SiGe
spin qubits [0.0]
We detect correlations in qubit-energy fluctuations of non-neighboring qubits defined in isotopically Si/SiGe quantum dots.
At low frequencies, the correlation coefficient reaches 10% for a next-nearest-neighbor qubit-pair separated by 200 nm.
arXiv Detail & Related papers (2023-02-23T00:45:30Z) - Experimental full network nonlocality with independent sources and
strict locality constraints [59.541438315564854]
Nonlocality in networks gives rise to phenomena radically different from that in standard Bell scenarios.
We experimentally observe full network nonlocality in a network where the source-independence, locality, and measurement-independence loopholes are closed.
Our experiment violates known inequalities characterizing non-full network nonlocal correlations by over five standard deviations.
arXiv Detail & Related papers (2023-02-05T20:03:58Z) - Regarding the Maximal Qubit Violations of n-Locality in Star and Chain
Networks [4.974890682815778]
Nonlocal correlations of noisy quantum systems are important for both understanding nature and developing quantum technology.
We consider the correlations of star and chain quantum networks where noisy entanglement sources are measured by nonsignaling parties.
arXiv Detail & Related papers (2022-12-13T21:39:24Z) - Persistency of non-n-local correlations in noisy linear networks [0.0]
Linear n-local networks are compatible with quantum repeaters based entanglement distribution protocols.
Error in entanglement generation, communication over noisy quantum channels and imperfections in measurements result in decay of quantumness across such networks.
arXiv Detail & Related papers (2022-08-14T14:47:07Z) - Characterizing low-frequency qubit noise [55.41644538483948]
Fluctuations of the qubit frequencies are one of the major problems to overcome on the way to scalable quantum computers.
The statistics of the fluctuations can be characterized by measuring the correlators of the outcomes of periodically repeated Ramsey measurements.
This work suggests a method that allows describing qubit dynamics during repeated measurements in the presence of evolving noise.
arXiv Detail & Related papers (2022-07-04T22:48:43Z) - The Optimal Noise in Noise-Contrastive Learning Is Not What You Think [80.07065346699005]
We show that deviating from this assumption can actually lead to better statistical estimators.
In particular, the optimal noise distribution is different from the data's and even from a different family.
arXiv Detail & Related papers (2022-03-02T13:59:20Z) - Stability of Neural Networks on Manifolds to Relative Perturbations [118.84154142918214]
Graph Neural Networks (GNNs) show impressive performance in many practical scenarios.
GNNs can scale well on large size graphs, but this is contradicted by the fact that existing stability bounds grow with the number of nodes.
arXiv Detail & Related papers (2021-10-10T04:37:19Z) - The Separation Capacity of Random Neural Networks [78.25060223808936]
We show that a sufficiently large two-layer ReLU-network with standard Gaussian weights and uniformly distributed biases can solve this problem with high probability.
We quantify the relevant structure of the data in terms of a novel notion of mutual complexity.
arXiv Detail & Related papers (2021-07-31T10:25:26Z)
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.