A neural network oracle for quantum nonlocality problems in networks

@article{Krivchy2019ANN,
  title={A neural network oracle for quantum nonlocality problems in networks},
  author={Tam{\'a}s Kriv{\'a}chy and Yu Cai and Daniel Cavalcanti and Arash Tavakoli and Nicolas Gisin and Nicolas Brunner},
  journal={npj Quantum Information},
  year={2019},
  volume={6},
  pages={1-7}
}
Characterizing quantum nonlocality in networks is a challenging, but important problem. Using quantum sources one can achieve distributions which are unattainable classically. A key point in investigations is to decide whether an observed probability distribution can be reproduced using only classical resources. This causal inference task is challenging even for simple networks, both analytically and using standard numerical techniques. We propose to use neural networks as numerical tools to… 
Reproduction and Behaviour of Local and Non-local Distribution
Quantum distributions cannot be reproduced classically. This sentence has already been proven with a neural network in [1] to be true, leading to the question of how else can such a distribution been
Neural-network approach for identifying nonclassicality from click-counting data
TLDR
An artificial neural network approach for the identification of nonclassical states of light based on recorded measurement statistics is presented and it is shown that it is capable of identifying some non classical states even if they were not used in the training phase.
Network Quantum Steering.
TLDR
This work promotes certain parties to be trusted and introduces the notion of network steering and network local hidden state (NLHS) models within this paradigm of independent sources, showing how the results from Bell nonlocality and quantum steering can be used to demonstrate network steering.
A reinforcement learning approach for quantum state engineering
TLDR
This work shows how classical reinforcement learning (RL) could be used as a tool for quantum state engineering (QSE), and provides a systematic algorithmic approach for using RL for quantum protocols that deal with a non-trivial continuous state space.
Quantum Correlations Take a New Shape A quantum network with a triangular geometry displays nonclassical correlations that appear to be fundamentally different from those so far revealed through Bell tests
∗Department of Mathematics, University of York, York, United Kingdom entangled states and separately measured. The measurement outcomes are found to be correlated more strongly than allowed by any
Ab-initio experimental violation of Bell inequalities
TLDR
This work proposes and implements a robust automated optimization approach based on the Stochastic Nelder-Mead algorithm that approaches the optimal Bell inequality violation after a limited number of iterations for a variety photonic states, measurement responses and Bell scenarios.
Fast semidefinite programming with feedforward neural networks
TLDR
This work proposes to solve feasibility semidefinite programs using artificial neural networks, and demonstrates that the trained neural network gives decent accuracy, while showing orders of magnitude increase in speed compared to a traditional solver.
Multistage games and Bell scenarios with communication
Bell nonlocality is a cornerstone of quantum theory with applications in information processing ranging from cryptography to distributed computing and game theory. Indeed, it is known that Bell's
Any star network of bipartite pure entangled states is genuine multipartite nonlocal
Quantum entanglement and nonlocality are inextricably linked. However, while entanglement is necessary for nonlocality, it is not always sufficient in the standard Bell scenario. We derive sufficient
High-speed batch processing of semidefinite programs with feedforward neural networks
Semidefinite programming is an important optimization task, often used in time-sensitive applications. Though they are solvable in polynomial time, in practice they can be too slow to be used in
...
1
2
...

References

SHOWING 1-10 OF 50 REFERENCES
Genuine Quantum Nonlocality in the Triangle Network.
TLDR
Novel examples of "quantum nonlocality without inputs" are presented, which are believed to represent a new form of quantum nonLocality, genuine to networks.
Machine Learning Nonlocal Correlations.
TLDR
Not only can the machine learn to quantify nonlocality, but discover new kinds of nonlocal correlations inaccessible with other current methods as well, and the framework is applied to distinguish between classical, quantum, and even postquantum correlations.
Nonlinear Bell Inequalities Tailored for Quantum Networks.
TLDR
An iterative procedure for constructing Bell inequalities tailored for networks is presented: starting from a given network, and a corresponding Bell inequality, the technique provides new Bell inequalities for a more complex network, involving one additional source and one additional observer.
Computationally Efficient Nonlinear Bell Inequalities for Quantum Networks.
  • M. Luo
  • Mathematics, Medicine
    Physical review letters
  • 2018
TLDR
This work proves the generic nonmultilocality of quantum networks with multiple independent observers using new Bell inequalities and suggests that the presented Bell inequalities can be used to characterize experimental quantum networks.
Active learning machine learns to create new quantum experiments
TLDR
An autonomous learning model is presented which learns to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments and improves the efficiency of their realization.
Limits on Correlations in Networks for Quantum and No-Signaling Resources.
TLDR
This work derives bounds on possible quantum correlations in a given network by deriving nonlinear inequalities that depend only on the topology of the network, and proves that these inequalities for the triangle network hold when the sources are arbitrary no-signaling boxes which can be wired together.
Constraints on nonlocality in networks from no-signaling and independence
TLDR
This work investigates constraints on correlations in networks under the natural assumptions of no-signaling and independence of the sources, and applies inflation technique to the no-input/binary-output triangle network, and shows that it admits non-trilocal distributions.
Bounding the Sets of Classical and Quantum Correlations in Networks.
TLDR
A method that allows the study of classical and quantum correlations in networks with causally independent parties, such as the scenario underlying entanglement swapping, is presented, which enables the use of the Navascués-Pironio-Acín hierarchy in complex quantum networks.
Information-theoretic implications of quantum causal structures.
TLDR
A general algorithm for computing information-theoretic constraints on the correlations that can arise from a given causal structure, where it allows for quantum systems as well as classical random variables.
Polynomial Bell Inequalities.
  • R. Chaves
  • Computer Science, Medicine
    Physical review letters
  • 2016
TLDR
This work provides a new, general, and conceptually clear method for the derivation of polynomial Bell inequalities in a wide class of scenarios, and shows how the construction can be used to allow for relaxations of causal constraints and naturally gives rise to a notion of nonsignaling in generalized Bell networks.
...
1
2
3
4
5
...