Controller-based Energy-Aware Wireless Sensor Network Routing using
Quantum Algorithms
- URL: http://arxiv.org/abs/2110.06321v1
- Date: Tue, 12 Oct 2021 20:16:21 GMT
- Title: Controller-based Energy-Aware Wireless Sensor Network Routing using
Quantum Algorithms
- Authors: Jie Chen, Prasanna Date, Nicholas Chancellor, Mohammed Atiquzzaman,
Cormac Sreenan
- Abstract summary: We show proof-of-principle for the use of a quantum processor instead of a classical processor, to find optimal or near-optimal solutions very quickly.
Preliminary results for small networks show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.
- Score: 15.607213703199209
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Energy efficient routing in wireless sensor networks has attracted attention
from researchers in both academia and industry, most recently motivated by the
opportunity to use SDN (software defined network)-inspired approaches. These
problems are NP-hard, with algorithms needing computation time which scales
faster than polynomial in the problem size. Consequently, heuristic algorithms
are used in practice, which are unable to guarantee optimally. In this short
paper, we show proof-of-principle for the use of a quantum annealing processor
instead of a classical processor, to find optimal or near-optimal solutions
very quickly. Our preliminary results for small networks show that this
approach using quantum computing has great promise and may open the door for
other significant improvements in the efficacy of network algorithms.
Related papers
- Coverage Analysis of Multi-Environment Q-Learning Algorithms for Wireless Network Optimization [18.035417008213077]
Recent advancements include ensemble multi-environment hybrid Q-learning algorithms.
We show that our algorithm can achieve %50 less policy error and %40 less runtime complexity than state-of-the-art reinforcement learning algorithms.
arXiv Detail & Related papers (2024-08-29T20:09:20Z) - Queue-aware Network Control Algorithm with a High Quantum Computing Readiness-Evaluated in Discrete-time Flow Simulator for Fat-Pipe Networks [0.0]
We introduce a resource reoccupation algorithm for traffic engineering in wide-area networks.
The proposed optimization algorithm changes traffic steering and resource allocation in case of overloaded transceivers.
We show that our newly introduced network simulator enables analyses of short-time effects like buffering within fat-pipe networks.
arXiv Detail & Related papers (2024-04-05T13:13:02Z) - Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking
Neural networks: from Algorithms to Technology [11.479629320025673]
spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications.
We describe advances in algorithmic and optimization innovations to efficiently train and scale low-latency, and energy-efficient SNNs.
We discuss the potential path forward for research in building deployable SNN systems.
arXiv Detail & Related papers (2023-12-02T19:47:00Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Quantum Approximate Optimization Algorithm for Bayesian network
structure learning [1.332091725929965]
In this work, a specific type of variational quantum algorithm, the quantum approximate optimization algorithm, was used to solve the Bayesian network structure learning problem.
Results showed that the quantum approximate optimization algorithm approach offers competitive results with state-of-the-art methods and quantitative resilience to quantum noise.
arXiv Detail & Related papers (2022-03-04T16:11:34Z) - Entanglement Rate Optimization in Heterogeneous Quantum Communication
Networks [79.8886946157912]
Quantum communication networks are emerging as a promising technology that could constitute a key building block in future communication networks in the 6G era and beyond.
Recent advances led to the deployment of small- and large-scale quantum communication networks with real quantum hardware.
In quantum networks, entanglement is a key resource that allows for data transmission between different nodes.
arXiv Detail & Related papers (2021-05-30T11:34:23Z) - Energy Efficient Edge Computing: When Lyapunov Meets Distributed
Reinforcement Learning [12.845204986571053]
In this work, we study the problem of energy-efficient offloading enabled by edge computing.
In the considered scenario, multiple users simultaneously compete for radio and edge computing resources.
The proposed solution also allows to increase the network's energy efficiency compared to a benchmark approach.
arXiv Detail & Related papers (2021-03-31T11:02:29Z) - Ps and Qs: Quantization-aware pruning for efficient low latency neural
network inference [56.24109486973292]
We study the interplay between pruning and quantization during the training of neural networks for ultra low latency applications.
We find that quantization-aware pruning yields more computationally efficient models than either pruning or quantization alone for our task.
arXiv Detail & Related papers (2021-02-22T19:00:05Z) - Data-Driven Random Access Optimization in Multi-Cell IoT Networks with
NOMA [78.60275748518589]
Non-orthogonal multiple access (NOMA) is a key technology to enable massive machine type communications (mMTC) in 5G networks and beyond.
In this paper, NOMA is applied to improve the random access efficiency in high-density spatially-distributed multi-cell wireless IoT networks.
A novel formulation of random channel access management is proposed, in which the transmission probability of each IoT device is tuned to maximize the geometric mean of users' expected capacity.
arXiv Detail & Related papers (2021-01-02T15:21:08Z) - 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)
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.