Analytical Performance Estimations for Quantum Repeater Network Scenarios
- URL: http://arxiv.org/abs/2407.11376v1
- Date: Tue, 16 Jul 2024 04:41:29 GMT
- Title: Analytical Performance Estimations for Quantum Repeater Network Scenarios
- Authors: Allen Zang, Joaquin Chung, Rajkumar Kettimuthu, Martin Suchara, Tian Zhong,
- Abstract summary: Quantum repeater chains will form the backbone of future quantum networks that distribute entanglement between network nodes.
By using Markov chains to model the dynamics in quantum repeater chains, we offer analytical estimations for long-run throughput and on-demand latency.
- Score: 1.9608333229350179
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum repeater chains will form the backbone of future quantum networks that distribute entanglement between network nodes. Therefore, it is important to understand the entanglement distribution performance of quantum repeater chains, especially their throughput and latency. By using Markov chains to model the stochastic dynamics in quantum repeater chains, we offer analytical estimations for long-run throughput and on-demand latency of continuous entanglement distribution. We first study single-link entanglement generation using general multiheralded protocols. We then model entanglement distribution with entanglement swapping over two links, using either a single- or a double-heralded entanglement generation protocol. We also demonstrate how the two-link results offer insights into the performance of general $2^k$-link nested repeater chains. Our results enrich the quantitative understanding of quantum repeater network performance, especially the dependence on system parameters. The analytical formulae themselves are valuable reference resources for the quantum networking community. They can serve as benchmarks for quantum network simulation validation or as examples of quantum network dynamics modeling using the Markov chain formalism.
Related papers
- Dissipation-driven quantum generative adversarial networks [11.833077116494929]
We introduce a novel dissipation-driven quantum generative adversarial network (DQGAN) architecture specifically tailored for generating classical data.
The classical data is encoded into the input qubits of the input layer via strong tailored dissipation processes.
We extract both the generated data and the classification results by measuring the observables of the steady state of the output qubits.
arXiv Detail & Related papers (2024-08-28T07:41:58Z) - On the Analysis of Quantum Repeater Chains with Sequential Swaps [7.885533851646292]
We evaluate the performance of two-way quantum repeater chains with sequential entanglement swapping.
We consider memory decoherence, gate imperfections, and imperfect link-level entanglement generation.
arXiv Detail & Related papers (2024-05-28T15:03:09Z) - Quantum Backbone Networks for Hybrid Quantum Dataframe Transmission [0.26217304977339473]
We elaborate on the design that uses entanglement and quantum teleportation to build the quantum backbone between packetized quantum networks.
We design a network interface to interconnect packetized quantum networks with entanglement-based quantum backbone networks.
For feasibility, we analyze various system parameters via simulation to benchmark the performance of the overall network.
arXiv Detail & Related papers (2024-04-29T09:07:44Z) - Asynchronous Entanglement Routing for the Quantum Internet [0.42855555838080833]
We propose a new set of asynchronous routing protocols for quantum networks.
The protocols update the entanglement-link asynchronous topologyly, identify optimal entanglement-swapping paths, and preserve unused direct-link entanglements.
Our results indicate that asynchronous protocols achieve a larger upper bound with an appropriate setting and significantly higher entanglement rate than existing synchronous approaches.
arXiv Detail & Related papers (2023-12-21T21:14:21Z) - Entangled Pair Resource Allocation under Uncertain Fidelity Requirements [59.83361663430336]
In quantum networks, effective entanglement routing facilitates communication between quantum source and quantum destination nodes.
We propose a resource allocation model for entangled pairs and an entanglement routing model with a fidelity guarantee.
Our proposed model can reduce the total cost by at least 20% compared to the baseline model.
arXiv Detail & Related papers (2023-04-10T07:16:51Z) - Adaptive, Continuous Entanglement Generation for Quantum Networks [59.600944425468676]
Quantum networks rely on entanglement between qubits at distant nodes to transmit information.
We present an adaptive scheme that uses information from previous requests to better guide the choice of randomly generated quantum links.
We also explore quantum memory allocation scenarios, where a difference in latency performance implies the necessity of optimal allocation of resources for quantum networks.
arXiv Detail & Related papers (2022-12-17T05:40:09Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Theory of Quantum Generative Learning Models with Maximum Mean
Discrepancy [67.02951777522547]
We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs)
We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution.
Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
arXiv Detail & Related papers (2022-05-10T08:05:59Z) - Entanglement Distribution in Multi-Platform Buffered-Router-Assisted
Frequency-Multiplexed Automated Repeater Chains [0.0]
We propose a quantum network architecture based on quantum processing devices based on NV$-$ colour centers.
Long-distance entanglement distribution is enabled by spectrally-multiplexed quantum repeaters based on rare-earth ion-doped crystals and imperfect entangled photon-pair sources.
arXiv Detail & Related papers (2021-06-08T20:25:43Z) - Quantum Federated Learning with Quantum Data [87.49715898878858]
Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems.
This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
arXiv Detail & Related papers (2021-05-30T12:19:27Z) - Entangling Quantum Generative Adversarial Networks [53.25397072813582]
We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN)
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
arXiv Detail & Related papers (2021-04-30T20:38:41Z)
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.