Building a Hierarchical Architecture and Communication Model for the Quantum Internet
- URL: http://arxiv.org/abs/2402.11806v2
- Date: Tue, 9 Jul 2024 06:36:28 GMT
- Title: Building a Hierarchical Architecture and Communication Model for the Quantum Internet
- Authors: Binjie He, Dong Zhang, Seng W. Loke, Shengrui Lin, Luke Lu,
- Abstract summary: The distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation & distribution.
We design a hierarchical quantum Internet architecture and a communication model to solve the problems above.
The evaluation results show that the entanglement distribution efficiency of hierarchical architecture is 11.5% higher than that of distributed architecture on average.
- Score: 7.794668853824469
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The research of architecture has tremendous significance in realizing quantum Internet. Although there is not yet a standard quantum Internet architecture, the distributed architecture is one of the possible solutions, which utilizes quantum repeaters or dedicated entanglement sources in a flat structure for entanglement preparation & distribution. In this paper, we analyze the distributed architecture in detail and demonstrate that it has three limitations: 1) possible high maintenance overhead, 2) possible low-performance entanglement distribution, and 3) unable to support optimal entanglement routing. We design a hierarchical quantum Internet architecture and a communication model to solve the problems above. We also present a W-state Based Centralized Entanglement Preparation & Distribution (W-state Based CEPD) scheme and a Centralized Entanglement Routing (CER) algorithm within our hierarchical architecture and perform an experimental comparison with other entanglement preparation & distribution schemes and entanglement routing algorithms within the distributed architecture. The evaluation results show that the entanglement distribution efficiency of hierarchical architecture is 11.5% higher than that of distributed architecture on average (minimum 3.3%, maximum 37.3%), and the entanglement routing performance of hierarchical architecture is much better than that of a distributed architecture according to the fidelity and throughput.
Related papers
- EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition [54.99121380536659]
Eye movement biometrics have received increasing attention thanks to its high secure identification.
Deep learning (DL) models have been recently successfully applied for eye movement recognition.
DL architecture still is determined by human prior knowledge.
We propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition.
arXiv Detail & Related papers (2024-09-22T13:11:08Z) - Comparison of Superconducting NISQ Architectures [0.0]
We study superconducting architectures including Google's Sycamore, IBM's Heavy-Hex, Rigetti's Aspen, and Ankaa.
We also study compilation tools that target these architectures.
arXiv Detail & Related papers (2024-09-03T17:12:08Z) - HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel
Neural Architecture Search [104.45426861115972]
We propose to directly generate structural parameters by utilizing the specifically designed hyper kernels.
We obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions.
A series of experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results.
arXiv Detail & Related papers (2023-04-23T17:27:40Z) - Domain-Specific Quantum Architecture Optimization [7.274584978257831]
We present a framework for optimizing quantum architectures, specifically through customizing qubit connectivity.
It is the first work that provides performance guarantees by integrating architecture optimization with an optimal compiler.
We demonstrate up to 59% fidelity improvement in simulation by optimizing the heavy-hexagon architecture for QAOA circuits, and up to 14% improvement on the grid architecture.
arXiv Detail & Related papers (2022-07-29T05:16:02Z) - Rethinking Architecture Selection in Differentiable NAS [74.61723678821049]
Differentiable Neural Architecture Search is one of the most popular NAS methods for its search efficiency and simplicity.
We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet.
We find that several failure modes of DARTS can be greatly alleviated with the proposed selection method.
arXiv Detail & Related papers (2021-08-10T00:53:39Z) - iDARTS: Differentiable Architecture Search with Stochastic Implicit
Gradients [75.41173109807735]
Differentiable ARchiTecture Search (DARTS) has recently become the mainstream of neural architecture search (NAS)
We tackle the hypergradient computation in DARTS based on the implicit function theorem.
We show that the architecture optimisation with the proposed method, named iDARTS, is expected to converge to a stationary point.
arXiv Detail & Related papers (2021-06-21T00:44:11Z) - Landmark Regularization: Ranking Guided Super-Net Training in Neural
Architecture Search [70.57382341642418]
Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware.
Recent works have empirically shown a ranking disorder between the performance of stand-alone architectures and that of the corresponding shared-weight networks.
We propose a regularization term that aims to maximize the correlation between the performance rankings of the shared-weight network and that of the standalone architectures.
arXiv Detail & Related papers (2021-04-12T09:32:33Z) - Adversarially Robust Neural Architectures [43.74185132684662]
This paper aims to improve the adversarial robustness of the network from the architecture perspective with NAS framework.
We explore the relationship among adversarial robustness, Lipschitz constant, and architecture parameters.
Our algorithm empirically achieves the best performance among all the models under various attacks on different datasets.
arXiv Detail & Related papers (2020-09-02T08:52:15Z) - Stage-Wise Neural Architecture Search [65.03109178056937]
Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications.
These networks consist of stages, which are sets of layers that operate on representations in the same resolution.
It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network.
However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
arXiv Detail & Related papers (2020-04-23T14:16:39Z) - Residual Attention Net for Superior Cross-Domain Time Sequence Modeling [0.0]
This paper serves as a proof-of-concept for a new architecture, with RAN aiming at providing the model a higher level understanding of sequence patterns.
We have achieved 35 state-of-the-art results with 10 results matching current state-of-the-art results without further model fine-tuning.
arXiv Detail & Related papers (2020-01-13T06:14:04Z)
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.