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A Review on Quantum Approximate Optimization Algorithm and its Variants

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arxiv 2306.09198 v2 pith:ZCWZRMZS submitted 2023-06-15 quant-ph

Kostas Blekos , Dean Brand , Andrea Ceschini , Chiao-Hui Chou , Rui-Hao Li , Komal Pandya , Alessandro Summer This is my paper · ORCID
classification quant-ph
keywords algorithmoptimizationquantumqaoaalgorithmsapproximatecombinatorialproblems
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The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve combinatorial optimization problems that are classically intractable. This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios, its applicability across various problem instances, and considerations of hardware-specific challenges such as error susceptibility and noise resilience. Additionally, we conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm. We aim to provide insights into key questions about the algorithm, such as whether it can outperform classical algorithms and under what circumstances it should be used. Towards this goal, we offer specific practical points in a form of a short guide. Keywords: Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Algorithms (VQAs), Quantum Optimization, Combinatorial Optimization Problems, NISQ Algorithms

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optimizing QUBO on a quantum computer by mimicking imaginary time evolution

    quant-ph 2025-05 conditional novelty 5.0 of 10

    ITEMC iteratively mimics imaginary time evolution to solve QUBO instances, achieving high CVaR-based approximation ratios in simulation and finding the best known solution on IBM hardware for up to 80 qubits.

  2. Efficient hybrid variational quantum algorithm for solving graph coloring problem

    quant-ph 2025-04 reject novelty 4.0 of 10

    A hierarchical hybrid QAOA algorithm for graph k-coloring partitions the graph, colors subgraphs quantumly and the interaction graph classically, and merges via feedback, but its iterative version succeeds in only 43....

  3. Transferring linearly fixed QAOA angles: performance and real device results

    quant-ph 2025-04 conditional novelty 4.0 of 10

    A fixed set of four linear QAOA angle coefficients trained on one random Ising instance transfers to other instances with only a small loss in approximation ratio, eliminating per-instance optimization.

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