Application of Quantum Approximate Optimization Algorithm in Solving the Total Domination Problem
- URL: http://arxiv.org/abs/2411.00364v1
- Date: Fri, 01 Nov 2024 05:05:14 GMT
- Title: Application of Quantum Approximate Optimization Algorithm in Solving the Total Domination Problem
- Authors: Haoqian Pan, Shiyue Wang, Changhong Lu,
- Abstract summary: Total Domination Problem (TDP) is a classic and critical example in the field.
Recent advancements in quantum computing have led to significant research into applying quantum algorithms to optimization problems.
We present a pioneering application of the Quantum Approximate Optimization Algorithm (QAOA) to tackle the TDP.
- Score: 0.5266869303483376
- License:
- Abstract: Recent advancements in quantum computing have led to significant research into applying quantum algorithms to combinatorial optimization problems. Among these challenges, the Total Domination Problem (TDP) is particularly noteworthy, representing a classic and critical example in the field. Since the last century, research efforts have focused on establishing its NP-completeness and developing algorithms for its resolution, which have been fundamental to combinatorial mathematics. Despite this rich history, the application of quantum algorithms to the TDP remains largely unexplored. In this study, we present a pioneering application of the Quantum Approximate Optimization Algorithm (QAOA) to tackle the TDP, evaluating its efficacy across a diverse array of parameters. Our experimental findings indicate that QAOA is effective in addressing the TDP; under most parameter combinations, it successfully computes the correct total dominating set (TDS). However, the algorithm's performance in identifying the optimal TDS is contingent upon the specific parameter choices, revealing a significant bias in the distribution of effective parameter points. This research contributes valuable insights into the potential of quantum algorithms for addressing the TDP and lays the groundwork for future investigations in this area.
Related papers
- Solving the Perfect Domination Problem by Quantum Approximate Optimization Algorithm with Small Layers [1.345977710890196]
The Perfect Domination Problem ( PDP) has significant applications in real-world scenarios, such as wireless and social networks.
With the recent advancements in quantum computing, there has been a surge in the development of quantum algorithms aimed at addressing NP-complete problems.
This study represents a pioneering effort to apply quantum algorithms to the PDP, as well as their efficacy in solving it.
arXiv Detail & Related papers (2024-11-19T16:12:36Z) - Solving the Independent Domination Problem by Quantum Approximate Optimization Algorithm [0.5919433278490629]
Independent Domination Problem (IDP) has practical implications in various real-world scenarios.
Existing classical algorithms for IDP are plagued by high computational complexity.
This paper introduces a Quantum Approximate Optimization (QAOA)-based approach to address the IDP.
arXiv Detail & Related papers (2024-10-22T17:49:00Z) - Quantum evolutionary algorithm for TSP combinatorial optimisation problem [0.0]
This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA)
We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA)
arXiv Detail & Related papers (2024-09-20T08:27:42Z) - 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) - 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) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - 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) - Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms [1.1682745573995112]
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era.
Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced.
arXiv Detail & Related papers (2021-12-15T18:55:02Z) - Hybrid Quantum Computing -- Tabu Search Algorithm for Partitioning
Problems: preliminary study on the Traveling Salesman Problem [0.8434687648198277]
We introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm.
Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses.
arXiv Detail & Related papers (2020-12-09T11:21:50Z) - 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) - Adaptive Sampling for Best Policy Identification in Markov Decision
Processes [79.4957965474334]
We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model.
The advantages of state-of-the-art algorithms are discussed and illustrated.
arXiv Detail & Related papers (2020-09-28T15:22:24Z)
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.