Quantum Graph Optimization Algorithm

• URL: http://arxiv.org/abs/2404.06434v1
• Date: Tue, 9 Apr 2024 16:25:07 GMT
• Title: Quantum Graph Optimization Algorithm
• Authors: Yuhan Huang, Ferris Prima Nugraha, Siyuan Jin, Yichi Zhang, Bei Zeng, Qiming Shao,
• Abstract summary: This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism. In terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA.
• Score: 7.788671046805509
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants, present a promising way, potentially exceeding the capabilities of classical algorithms, for addressing QUBO tasks. However, the possibility of using message-passing machines, inspired by classical graph neural networks, to enhance the power and performance of these quantum algorithms for QUBO tasks was not investigated. This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism, which demonstrates significant improvements in performance for solving QUBO problems in terms of resource efficiency and solution precision, compared to QAOA, its variants, and other quantum graph neural networks. Furthermore, in terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA, presenting a substantial advancement in the field of quantum approximate optimization.

Related papers

• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm [14.554010382366302]
  Quantum Approximate Optimization (QAOA) stands out for its potential to efficiently solve the Max-Cut problem. We use Graph Neural Networks (GNN) as a warm-start technique to optimize QAOA, using GNN as a warm-start technique. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing.
  arXiv  Detail & Related papers  (2024-03-05T20:23:25Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems [65.268245109828]
  We compare the performance of classicals across a series of partially-randomized tasks. We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
  arXiv  Detail & Related papers  (2023-10-14T02:13:26Z)
• Quantum-Informed Recursive Optimization Algorithms [0.0]
  We propose and implement a family of quantum-informed recursive optimization (QIRO) algorithms for optimization problems. Our approach leverages quantum resources to obtain information that is used in problem-specific classical reduction steps. We use backtracking techniques to further improve the performance of the algorithm without increasing the requirements on the quantum hardware.
  arXiv  Detail & Related papers  (2023-08-25T18:02:06Z)
• A Review on Quantum Approximate Optimization Algorithm and its Variants [47.89542334125886]
  The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve intractable optimization problems. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios. We conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm.
  arXiv  Detail & Related papers  (2023-06-15T15:28:12Z)
• A Comparative Study On Solving Optimization Problems With Exponentially Fewer Qubits [0.0]
  We evaluate and improve an algorithm based on Variational Quantum Eigensolver (VQE) We highlight the numerical instabilities generated by encoding the problem into the variational ansatz. We propose a classical optimization procedure to find the ground-state of the ansatz in less iterations with a better objective.
  arXiv  Detail & Related papers  (2022-10-21T08:54:12Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• Quantum variational optimization: The role of entanglement and problem hardness [0.0]
  We study the role of entanglement, the structure of the variational quantum circuit, and the structure of the optimization problem. Our numerical results indicate an advantage in adapting the distribution of entangling gates to the problem's topology. We find evidence that applying conditional value at risk type cost functions improves the optimization, increasing the probability of overlap with the optimal solutions.
  arXiv  Detail & Related papers  (2021-03-26T14:06:54Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.