Multi-Objective Routing Optimization Using Coherent Ising Machine in Wireless Multihop Networks
- URL: http://arxiv.org/abs/2503.07924v1
- Date: Mon, 10 Mar 2025 23:59:50 GMT
- Title: Multi-Objective Routing Optimization Using Coherent Ising Machine in Wireless Multihop Networks
- Authors: Yu-Xuan Lin, Chu-Yao Xu, Chuan Wang,
- Abstract summary: Coherent Ising Machines (CIM) is a quantum-inspired algorithm for multi-objective routing optimization in wireless networks.<n> CIM demonstrates strong scalability across diverse network topologies without requiring topology-specific adjustments.<n>Our results show that CIM provides feasible and near-optimal solutions for networks containing hundreds of nodes and thousands of edges.
- Score: 4.4727509471456495
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-objective combinatorial optimization in wireless communication networks is a challenging task, particularly for large-scale and diverse topologies. Recent advances in quantum computing offer promising solutions for such problems. Coherent Ising Machines (CIM), a quantum-inspired algorithm, leverages the quantum properties of coherent light, enabling faster convergence to the ground state. This paper applies CIM to multi-objective routing optimization in wireless multi-hop networks. We formulate the routing problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem and map it onto an Ising model, allowing CIM to solve it. CIM demonstrates strong scalability across diverse network topologies without requiring topology-specific adjustments, overcoming the limitations of traditional quantum algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). Our results show that CIM provides feasible and near-optimal solutions for networks containing hundreds of nodes and thousands of edges. Additionally, a complexity analysis highlights CIM's increasing efficiency as network size grows
Related papers
- Distributed Variational Quantum Algorithm with Many-qubit for Optimization Challenges [0.25782420501870296]
Existing quantum algorithms struggle with scalability and accuracy due to excessive reliance on entanglement.<n>We propose variational quantum optimization algorithm (VQOA), which leverages many-qubit (MQ) operations in an ansatz solely employing quantum superposition.<n>We also introduce distributed VQOA, which integrates high-performance computing with quantum computing to achieve superior performance across MQ systems and classical nodes.
arXiv Detail & Related papers (2025-02-28T22:13:23Z) - Resource-Efficient Compilation of Distributed Quantum Circuits for Solving Large-Scale Wireless Communication Network Problems [10.434368470402935]
optimizing routing in Wireless Sensor Networks (WSNs) is pivotal for minimizing energy consumption and extending network lifetime.<n>This paper introduces a resourceefficient compilation method for distributed quantum circuits tailored to address large-scale WSN routing problems.
arXiv Detail & Related papers (2025-01-17T15:10:22Z) - MG-Net: Learn to Customize QAOA with Circuit Depth Awareness [51.78425545377329]
Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling optimization challenges.
The requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices.
We introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians.
arXiv Detail & Related papers (2024-09-27T12:28:18Z) - Surrogate-guided optimization in quantum networks [0.9148747049384086]
We propose an optimization algorithm to improve the design and performance of quantum communication networks.
Our framework allows for more comprehensive quantum network studies, integrating surrogate-assisted optimization with existing quantum network simulators.
arXiv Detail & Related papers (2024-07-24T11:55:18Z) - Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization [0.8121127831316319]
We propose a method for constructing quantum circuits which greatly reduces inter-device communication costs.
We show that we can construct tractable circuits nearly three times the size of previous QDCA methods while retaining a similar or greater level of quality.
arXiv Detail & Related papers (2024-05-01T20:49:50Z) - Multi-Objective Optimization and Network Routing with Near-Term Quantum
Computers [0.2150989251218736]
We develop a scheme with which near-term quantum computers can be applied to solve multi-objective optimization problems.
We focus on an implementation based on the quantum approximate optimization algorithm (QAOA)
arXiv Detail & Related papers (2023-08-16T09:22:01Z) - 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) - 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) - Joint Deep Reinforcement Learning and Unfolding: Beam Selection and
Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention.
In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
arXiv Detail & Related papers (2021-01-05T03:55:04Z) - 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.