Influence of noise in entanglement-based quantum networks
- URL: http://arxiv.org/abs/2305.03759v2
- Date: Wed, 28 Aug 2024 14:54:11 GMT
- Title: Influence of noise in entanglement-based quantum networks
- Authors: Maria Flors Mor-Ruiz, Wolfgang Dür,
- Abstract summary: We consider entanglement-based quantum networks, where multipartite entangled resource states are distributed and stored among the nodes.
We study the influence of noise in this process, where we consider imperfections in state preparation, memories, and measurements.
We find that in large networks, high-dimensional cluster states are favorable and lead to a significantly higher target state fidelity.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider entanglement-based quantum networks, where multipartite entangled resource states are distributed and stored among the nodes and locally manipulated upon request to establish the desired target configuration. Separating the generation process from the requests enables a pre-preparation of resources, hence a reduced network latency. It also allows for an optimization of the entanglement topology, which is independent of the underlying network geometry. We concentrate on establishing Bell pairs or tripartite GHZ states between arbitrary parties. We study the influence of noise in this process, where we consider imperfections in state preparation, memories, and measurements - all of which can be modeled by local depolarizing noise. We compare different resource states corresponding to linear chains, trees, or multi-dimensional rectangular clusters, as well as centralized topologies using bipartite or tripartite entangled states. We compute the fidelity of the target states using a recently established efficient method, the noisy stabilizer formalism, and identify the best resource states within these classes. This allows us to treat networks of large size containing millions of nodes. We find that in large networks, high-dimensional cluster states are favorable and lead to a significantly higher target state fidelity.
Related papers
- Imperfect quantum networks with tailored resource states [0.0]
Entanglement-based quantum networks exhibit a unique flexibility in the choice of entangled resource states.
We study how the flexibility of this approach can be used for the distribution of entanglement in a fully asymmetric network scenario.
arXiv Detail & Related papers (2024-03-28T19:00:02Z) - Noise-robust proofs of quantum network nonlocality [0.49157446832511503]
We present noise-robust proofs of network quantum nonlocality, for a class of quantum distributions on the triangle network.
Our work opens interesting perspectives towards the practical implementation of quantum network nonlocality.
arXiv Detail & Related papers (2023-11-03T18:25:59Z) - Multiparty Entanglement Routing in Quantum Networks [0.0]
A protocol is proposed for extracting maximally entangled (GHZn) states for any number of parties in quantum networks.
The protocol only requires local measurements at the network nodes and just a single qubit memory per user.
arXiv Detail & Related papers (2022-11-12T15:40:34Z) - Optimal quantum control via genetic algorithms for quantum state
engineering in driven-resonator mediated networks [68.8204255655161]
We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms.
We consider a network of qubits -- encoded in the states of artificial atoms with no direct coupling -- interacting via a common single-mode driven microwave resonator.
We observe high quantum fidelities and resilience to noise, despite the algorithm being trained in the ideal noise-free setting.
arXiv Detail & Related papers (2022-06-29T14:34:00Z) - Optimized Quantum Networks [68.8204255655161]
Quantum networks offer the possibility to generate different kinds of entanglement prior to network requests.
We utilize this to design entanglement-based quantum networks tailored to their desired functionality.
arXiv Detail & Related papers (2021-07-21T18:00:07Z) - Full network nonlocality [68.8204255655161]
We introduce the concept of full network nonlocality, which describes correlations that necessitate all links in a network to distribute nonlocal resources.
We show that the most well-known network Bell test does not witness full network nonlocality.
More generally, we point out that established methods for analysing local and theory-independent correlations in networks can be combined in order to deduce sufficient conditions for full network nonlocality.
arXiv Detail & Related papers (2021-05-19T18:00:02Z) - Creating and concentrating quantum resource states in noisy environments
using a quantum neural network [2.834895018689047]
We provide a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes.
We show that our method is robust and can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states.
In very noisy systems, where noise is comparable to the driving strength, we show how to concentrate entanglement by mixing more states in a larger network.
arXiv Detail & Related papers (2021-01-15T07:18:06Z) - Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users.
We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
arXiv Detail & Related papers (2020-11-23T19:00:01Z) - Heterogeneous Multipartite Entanglement Purification for
Size-Constrained Quantum Devices [68.8204255655161]
Purifying entanglement resources after their imperfect generation is an indispensable step towards using them in quantum architectures.
Here we depart from the typical purification paradigm for multipartite states explored in the last twenty years.
We find that smaller sacrificial' states, like Bell pairs, can be more useful in the purification of multipartite states than additional copies of these same states.
arXiv Detail & Related papers (2020-11-23T19:00:00Z) - Resource Allocation via Graph Neural Networks in Free Space Optical
Fronthaul Networks [119.81868223344173]
This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks.
We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure.
The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
arXiv Detail & Related papers (2020-06-26T14:20:48Z)
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.