Scaling Limits of Quantum Repeater Networks
- URL: http://arxiv.org/abs/2305.08696v2
- Date: Wed, 26 Jul 2023 15:02:26 GMT
- Title: Scaling Limits of Quantum Repeater Networks
- Authors: Mahdi Chehimi, Shahrooz Pouryousef, Nitish K. Panigrahy, Don Towsley,
and Walid Saad
- Abstract summary: Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing.
Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability.
In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
- Score: 62.75241407271626
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum networks (QNs) are a promising platform for secure communications,
enhanced sensing, and efficient distributed quantum computing. However, due to
the fragile nature of quantum states, these networks face significant
challenges in terms of scalability. In this paper, the scaling limits of
quantum repeater networks (QRNs) are analyzed. The goal of this work is to
maximize the overall length, or scalability of QRNs such that long-distance
quantum communications is achieved while application-specific
quality-of-service (QoS) requirements are satisfied. In particular, a novel
joint optimization framework that aims at maximizing QRN scalability, while
satisfying QoS constraints on the end-to-end fidelity and rate is proposed. The
proposed approach optimizes the number of QRN repeater nodes, their separation
distance, and the number of distillation rounds to be performed at both link
and end-to-end levels. Extensive simulations are conducted to analyze the
tradeoffs between QRN scalability, rate, and fidelity under gate and
measurement errors. The obtained results characterize the QRN scaling limits
for a given QoS requirement. The proposed approach offers a promising solution
and design guidelines for future QRN deployments.
Related papers
- Exact Quantum Algorithm for Unit Commitment Optimization based on Partially Connected Quantum Neural Networks [12.688426228429604]
In this paper, we focus on the implement of the unit commitment problem by exact quantum algorithms based on the quantum neural network (QNN)
The results show that the exact solutions can be obtained by the improved algorithm and the depth of the quantum circuit can be reduced simultaneously.
arXiv Detail & Related papers (2024-11-18T08:29:50Z) - SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation [12.591173729459427]
We show that it is possible to leverage a pre-trained neural network to directly generate the QSP circuit for arbitrary quantum state.
Our study makes a steady step towards a universal neural designer for approximate QSP.
arXiv Detail & Related papers (2024-08-10T04:39:05Z) - A joint optimization approach of parameterized quantum circuits with a
tensor network [0.0]
Current intermediate-scale quantum (NISQ) devices remain limited in their capabilities.
We propose the use of parameterized Networks (TNs) to attempt an improved performance of the Variational Quantum Eigensolver (VQE) algorithm.
arXiv Detail & Related papers (2024-02-19T12:53:52Z) - Reconfigurable Intelligent Surface (RIS)-Assisted Entanglement
Distribution in FSO Quantum Networks [62.87033427172205]
Quantum networks (QNs) relying on free-space optical (FSO) quantum channels can support quantum applications in environments where establishing an optical fiber infrastructure is challenging and costly.
A reconfigurable intelligent surface (RIS)-assisted FSO-based QN is proposed as a cost-efficient framework providing a virtual line-of-sight between users for entanglement distribution.
arXiv Detail & Related papers (2024-01-19T17:16:40Z) - Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud
Computing [73.7522199491117]
Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources.
The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning.
We propose a resource allocation model to provision quantum computing and networking resources.
arXiv Detail & Related papers (2023-07-25T00:38:46Z) - Matching Game for Optimized Association in Quantum Communication
Networks [65.16483325184237]
This paper proposes a swap-stable request-QS association algorithm for quantum switches.
It achieves a near-optimal (within 5%) performance in terms of the percentage of served requests.
It is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.
arXiv Detail & Related papers (2023-05-22T03:39:18Z) - Quantum Federated Learning with Entanglement Controlled Circuits and
Superposition Coding [44.89303833148191]
We develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs)
We propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs.
In an image classification task, extensive simulations corroborate the effectiveness of eSQFL.
arXiv Detail & Related papers (2022-12-04T03:18:03Z) - Green, Quantized Federated Learning over Wireless Networks: An
Energy-Efficient Design [68.86220939532373]
The finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format.
The proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm.
arXiv Detail & Related papers (2022-07-19T16:37:24Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z)
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.