Proceedings Article10.1109/ICTC46691.2019.8939749
A Tutorial on Quantum Approximate Optimization Algorithm (QAOA): Fundamentals and Applications
Jaeho Choi,Joongheon Kim +1 more
- 01 Oct 2019
- pp 138-142
75
TL;DR: This paper explains the applications of QAOA to major combinatorial optimization problems such as maximum cut (MaxCut) problem and the max-independent set (MIS) problem.
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Abstract: Over the past few years, many researchers around the world have been keen to know the potential and efficiency of quantum computers. The researchers have focused on specific issues that classical computers cannot solve or issues that quantum computers can handle in a better way. Among these various attractive research topics in quantum computers, this paper introduces the Quantum Approximate Optimization Algorithm (QAOA) which guarantees relatively considerable performances in many combinatorial optimization problems. For the comprehensive understanding of QAOA, this paper also describes the approximate optimization, the Quantum Alternating Operator Ansatz, and applications. Besides the theories of QAOA and Quantum Alternating Operator Ansatz, this paper explains the applications of QAOA to major combinatorial optimization problems such as maximum cut (MaxCut) problem and the max-independent set (MIS) problem.
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Citations
Quantum Neural Networks: Concepts, Applications, and Challenges
Yunseok Kwak,Won Joon Yun,Soyi Jung,Joongheon Kim +3 more
- 17 Aug 2021
TL;DR: In this article, the authors discuss the challenges of quantum deep learning research in multiple perspectives and present various future research directions and application fields of quantum DNNs, as well as their major achievements.
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Peer Review
A Review on Quantum Approximate Optimization Algorithm and its Variants
Kostas Blekos,D. Brand,A. Ceschini,Chia-Hui Chou,Rui Li,Komal Pandya,Alessandro Summer +6 more
- 15 Jun 2023
TL;DR: The Quantum Approximate Optimization Algorithm (QAOA) as discussed by the authors is a variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable.
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Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation.
TL;DR: In this paper, the authors introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation, and also discuss the power and possibility of quantum RL from the experimental results obtained through this work.
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Models in quantum computing: a systematic review
TL;DR: In this paper, the authors provide an insight into quantum computing models coupled with the identification of some pros and cons, and provide new classifications of quantum models based on the literature reviewed and links results to that of the four major categories of quantum computing.
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TL;DR: In this paper, a quantum optimization based algorithm is used to minimize overlapping monitoring areas among observation satellite constellation, where the overlapping can be modeled via a max-weight independent set (MWIS) problem, which is one of well-known NP-hard problems.
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References
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TL;DR: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future as mentioned in this paper, which will be useful tools for exploring many-body quantum physics, and may have other useful applications.
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Approximation Algorithms
Vijay V. Vazirani
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TL;DR: Covering the basic techniques used in the latest research work, the author consolidates progress made so far, including some very recent and promising results, and conveys the beauty and excitement of work in the field.
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Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
TL;DR: This algorithm gives the first substantial progress in approximating MAX CUT in nearly twenty years, and represents the first use of semidefinite programming in the design of approximation algorithms.
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A Quantum Approximate Optimization Algorithm
TL;DR: A quantum algorithm that produces approximate solutions for combinatorial optimization problems that depends on a positive integer p and the quality of the approximation improves as p is increased, and is studied as applied to MaxCut on regular graphs.
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