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The Quantum Approximate Optimization Algorithm (QAOA) is a promising variational quantum algorithm introduced to tackle classically intractable combinatorial optimization problems. This tutorial offers a comprehensive, first-principles…

量子物理 · 物理学 2025-11-25 Alessandro Giovagnoli

This article consists of a short introduction to the quantum approximation optimisation algorithm (QAOA). The mathematical structure of the QAOA, as well as its basic properties, are described. The implementation of the QAOA on MaxCut…

量子物理 · 物理学 2021-03-25 Behzad Mansouri

This paper presents a numerical simulation investigation of the Warm-Start Quantum Approximate Optimization Algorithm (QAOA) as proposed by Tate et al. [1], focusing on its application to 3-regular Max-Cut problems. Our study demonstrates…

量子物理 · 物理学 2024-09-16 Sean Feeney , Reuben Tate , Stephan Eidenbenz

Quantum Approximation Optimization Algorithm (QAOA) is a highly advocated variational algorithm for solving the combinatorial optimization problem. One critical feature in the quantum circuit of QAOA algorithm is that it consists of…

量子物理 · 物理学 2022-07-21 Yuwei Jin , Jason Luo , Lucent Fong , Yanhao Chen , Ari B. Hayes , Chi Zhang , Fei Hua , Eddy Z. Zhang

Hardware errors are a major obstacle to demonstrating quantum advantage with the quantum approximate optimization algorithm (QAOA). Recently, symmetry verification has been proposed and empirically demonstrated to boost the quantum state…

量子物理 · 物理学 2023-03-20 Ashish Kakkar , Jeffrey Larson , Alexey Galda , Ruslan Shaydulin

The Quantum Approximate Optimization Algorithm (QAOA) is a quantum-classical hybrid algorithm intending to find the ground state of a target Hamiltonian. Theoretically, QAOA can obtain the approximate solution if the quantum circuit is deep…

量子物理 · 物理学 2022-04-26 Yahui Chai , Yong-Jian Han , Yu-Chun Wu , Ye Li , Menghan Dou , Guo-Ping Guo

The Quantum Approximate Optimization Algorithm (QAOA) has been one of the leading candidates for near-term quantum advantage in gate-model quantum computers. From its inception, this algorithm has sparked the desire for comparison between…

量子物理 · 物理学 2021-12-08 Colin Campbell , Edward Dahl

The quantum approximate optimization algorithm (QAOA) is a near-term quantum algorithm aimed at solving combinatorial optimization problems. Since its introduction, various generalizations have emerged, spanning modifications to the initial…

量子物理 · 物理学 2024-11-18 Truman Yu Ng , Jin Ming Koh , Dax Enshan Koh

Circuit cutting was originally designed to retrieve the expectation value of an observable with respect to a large quantum circuit by executing smaller circuit fragments. In this work, however, we demonstrate the application of circuit…

量子物理 · 物理学 2025-07-10 Friedrich Wagner , Christian Ufrecht , Martin Braun , Daniel D. Scherer

The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles…

量子物理 · 物理学 2026-02-26 Simon Thelen , Hila Safi , Wolfgang Mauerer

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. Here we show that the \emph{density} of problem constraints versus problem variables acts as a performance…

量子物理 · 物理学 2021-08-31 V. Akshay , H. Philathong , I. Zacharov , J. Biamonte

Quantum computing holds promise for outperforming classical computing in specialized applications such as optimization. With current Noisy Intermediate Scale Quantum (NISQ) devices, only variational quantum algorithms like the Quantum…

量子物理 · 物理学 2024-07-08 Daniel Müssig , Markus Wappler , Steve Lenk , Jörg Lässig

An important task in multi-objective optimization is generating the Pareto front -- the set of all Pareto-optimal compromises among multiple objective functions applied to the same set of variables. Since this task can be computationally…

量子物理 · 物理学 2025-11-04 Andrew D. King

The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices. In QAOA, the objective is a function of the…

量子物理 · 物理学 2019-07-24 Mahabubul Alam , Abdullah Ash-Saki , Swaroop Ghosh

The performance of the Quantum Approximate Optimisation Algorithm (QAOA) relies on the setting of optimal parameters in each layer of the circuit. This is no trivial task, and much literature has focused on the challenge of finding optimal…

量子物理 · 物理学 2024-02-14 Vivek Katial , Kate Smith-Miles , Charles Hill

The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising Noisy Intermediate Quantum Algorithms (NISQ) in solving combinatorial optimizations and displays potential over classical heuristic techniques.…

量子物理 · 物理学 2024-01-18 Arul Rhik Mazumder , Anuvab Sen , Udayon Sen

In this paper we consider the scalability of Multi-Angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets.…

新兴技术 · 计算机科学 2024-08-16 Igor Gaidai , Rebekah Herrman

The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks. However, an external parameter optimization required in QAOA could become a…

量子物理 · 物理学 2022-01-26 Stefan H. Sack , Maksym Serbyn

The quantum-classical hybrid algorithm is an algorithm that holds promise in demonstrating the quantum advantage in NISQ devices. When running such algorithms, effects from quantum noise are inevitable. In our work, we consider a well-known…

量子物理 · 物理学 2019-12-10 Cheng Xue , Zhao-Yun Chen , Yu-Chun Wu , Guo-Ping Guo

The Quantum Approximate Optimization Algorithm (QAOA) requires that circuit parameters are determined that allow one to sample from high-quality solutions to combinatorial optimization problems. Such parameters can be obtained using either…

量子物理 · 物理学 2023-01-11 David Headley , Frank K. Wilhelm