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The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which…

Quantum Physics · Physics 2020-08-13 James Ostrowski , Rebekah Herrman , Travis S. Humble , George Siopsis

We prove that \Omega(n log(n)) comparisons are necessary for any quantum algorithm that sorts n numbers with high success probability and uses only comparisons. If no error is allowed, at least 0.110nlog_2(n) - 0.067n + O(1) comparisons…

Quantum Physics · Physics 2007-05-23 Yaoyun Shi

High error rates and limited fidelity of quantum gates in near-term quantum devices are the central obstacles to successful execution of the Quantum Approximate Optimization Algorithm (QAOA). In this paper we introduce an…

Quantum Physics · Physics 2022-06-16 Ruslan Shaydulin , Alexey Galda

Optimization is often cited as a promising application of quantum computers. However, the low degree of provable quantum speedups has led prior rigorous end-to-end resource analyses to conclude that a quantum computer is unlikely to surpass…

Universal approximation theorems are the foundations of classical neural networks, providing theoretical guarantees that the latter are able to approximate maps of interest. Recent results have shown that this can also be achieved in a…

Quantum Physics · Physics 2025-04-14 Lukas Gonon , Antoine Jacquier

The quantum approximate optimization algorithm (QAOA) is known for its capability and universality in solving combinatorial optimization problems on near-term quantum devices. The results yielded by QAOA depend strongly on its initial…

Quantum Physics · Physics 2022-09-29 Xinwei Lee , Ningyi Xie , Yoshiyuki Saito , Dongsheng Cai , Nobuyoshi Asai

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm to solve binary-variable optimization problems. Due to the short circuit depth and its expected robustness to systematic errors, it is one of the…

Quantum Physics · Physics 2022-08-23 Jason Larkin , Matías Jonsson , Daniel Justice , Gian Giacomo Guerreschi

A key open question in quantum computing is whether quantum algorithms can potentially offer a significant advantage over classical algorithms for tasks of practical interest. Understanding the limits of classical computing in simulating…

Quantum Physics · Physics 2021-06-23 Matija Medvidovic , Giuseppe Carleo

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical variational algorithm that offers the potential to handle combinatorial optimization problems. Introducing constraints in such combinatorial optimization…

Quantum Physics · Physics 2021-12-15 Santosh Kumar Radha

This paper describes an application of the Quantum Approximate Optimisation Algorithm (QAOA) to efficiently find approximate solutions for computational problems contained in the polynomially bounded NP optimisation complexity class (NPO…

Quantum Physics · Physics 2021-07-28 Samuel Marsh , Jingbo Wang

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…

Quantum Physics · Physics 2022-04-26 Yahui Chai , Yong-Jian Han , Yu-Chun Wu , Ye Li , Menghan Dou , Guo-Ping Guo

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here,…

The quantum approximate optimization algorithm (QAOA) has been introduced as a heuristic digital quantum computing scheme to find approximate solutions of combinatorial problems with shallow circuits. We present a scheme to parallelize this…

Quantum Physics · Physics 2018-03-02 Wolfgang Lechner

We establish and discuss a number of connections between a digitized version of Quantum Annealing (QA) with the Quantum Approximate Optimization Algorithm (QAOA) introduced by Farhi et al. (arXiv:1411.4028) as an alternative hybrid…

Quantum Physics · Physics 2019-12-10 Glen Bigan Mbeng , Rosario Fazio , Giuseppe Santoro

Many combinatorial optimization problems admit a maximin fairness variant, where the aim is to find a distribution over possible solutions which maximizes an expected worst-case outcome. However, the support for an optimal distribution may…

Quantum Physics · Physics 2026-04-17 Bao Bach , Cameron Ibrahim , Reuben Tate , Jad Salem , Stephan Eidenbenz , Ilya Safro

We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a…

Emerging Technologies · Computer Science 2020-04-07 Mahabubul Alam , Abdullah Ash-Saki , Swaroop Ghosh

The Quantum Approximate Optimization Algorithm (QAOA) is widely studied for combinatorial optimization and has achieved significant advances both in theoretical guarantees and practical performance, yet for general combinatorial…

Quantum Physics · Physics 2025-11-26 Jingheng Wang , Shengminjie Chen , Xiaoming Sun , Jialin Zhang

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…

Quantum Physics · Physics 2026-02-26 Simon Thelen , Hila Safi , Wolfgang Mauerer

Quantum Approximate Optimization Algorithm (QAOA) is a leading candidate algorithm for solving combinatorial optimization problems on quantum computers. However, in many cases QAOA requires computationally intensive parameter optimization.…

Quantum algorithms for binary optimization problems have been the subject of extensive study. However, the application of quantum algorithms to integer optimization problems remains comparatively unexplored. In this paper, we study the…