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The practical use of many types of near-term quantum computers requires accounting for their limited connectivity. One way of overcoming limited connectivity is to insert swaps in the circuit so that logical operations can be performed on…

Quantum Physics · Physics 2019-05-14 Bryan O'Gorman , William J. Huggins , Eleanor G. Rieffel , K. Birgitta Whaley

Many quantum algorithms seek to output a specific bitstring solving the problem of interest--or a few if the solution is degenerate. It is the case for the quantum approximate optimization algorithm (QAOA) in the limit of large circuit…

Quantum Physics · Physics 2022-08-23 Maxime Dupont , Nicolas Didier , Mark J. Hodson , Joel E. Moore , Matthew J. Reagor

We investigate the Maximum Cut (MaxCut) problem on different graph classes with the Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular, heuristics on the relationship between graph symmetries and the…

Quantum Physics · Physics 2025-11-04 Leonardo Lavagna , Simone Piperno , Andrea Ceschini , Massimo Panella

The quantum approximate optimization algorithm (QAOA) promises to solve classically intractable computational problems in the area of combinatorial optimization. A growing amount of evidence suggests that the originally proposed form of the…

Quantum Physics · Physics 2022-05-04 Michelle Chalupnik , Hans Melo , Yuri Alexeev , Alexey Galda

The Quantum Approximate Optimisation Algorithm (QAOA) is a leading candidate for near-term quantum advantage, yet its practical impact is hindered by limited performance on symmetric local Hamiltonians and the costly optimisation of…

The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. For a given problem instance,…

Quantum Physics · Physics 2026-02-23 Tianyi Hao , Zichang He , Ruslan Shaydulin , Jeffrey Larson , Marco Pistoia

In this work, we compare the performance of the Quantum Approximate Optimization Algorithm (QAOA) with state-of-the-art classical solvers such as Gurobi and MQLib to solve the combinatorial optimization problem MaxCut on 3-regular graphs.…

Quantum Physics · Physics 2024-05-01 Danylo Lykov , Jonathan Wurtz , Cody Poole , Mark Saffman , Tom Noel , Yuri Alexeev

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate…

Quantum Physics · Physics 2023-11-14 Danylo Lykov , Ruslan Shaydulin , Yue Sun , Yuri Alexeev , Marco Pistoia

Quantum circuits are typically represented by a (ordered) sequence of gates over a set of virtual qubits. During compilation, the virtual qubits of the gates are assigned to the physical qubits of the underlying quantum hardware, a step…

While the ultimate goal of solving computationally intractable problems is to find a provably optimal solutions, practical constraints of real-world scenarios often necessitate focusing on efficiently obtaining high-quality, near-optimal…

Quantum Physics · Physics 2025-04-23 Prashanti Priya Angara , Emily Martins , Ulrike Stege , Hausi Müller

Quantum computers promise to outperform their classical counterparts at certain tasks. However, existing quantum devices are error-prone and restricted in size. Thus, effective compilation methods are crucial to exploit limited quantum…

Optimization and Control · Mathematics 2025-07-17 Moritz Stargalla , Friedrich Wagner

We study a modified fixed-point version of the Quantum Approximate Optimization Algorithm (fpQAOA), in which parameters are trained on small instances and transferred to larger problems. Our scheme combines three key ingredients: (i)…

The quantum approximate optimization algorithm (QAOA) has the potential to approximately solve complex combinatorial optimization problems in polynomial time. However, current noisy quantum devices cannot solve large problems due to…

The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm that can be used to approximately solve combinatorial optimization problems. However, a major limitation of QAOA is that it is a "local" algorithm for…

Quantum Physics · Physics 2025-04-10 Quinn Langfitt , Reuben Tate , Stephan Eidenbenz

We introduce a relax-and-round approach embedding the quantum approximate optimization algorithm (QAOA) with $p\geq 1$ layers. We show for many problems, including Sherrington-Kirkpatrick spin glasses, that at $p=1$, it is as accurate as…

Quantum Physics · Physics 2024-01-25 Maxime Dupont , Bhuvanesh Sundar

The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems on noisy intermediate-scale quantum (NISQ) hardware. Making NISQ implementations of the QAOA resilient to noise requires short ansatz…

The Quantum Approximate Optimization Algorithm (QAOA) is a leading framework for quantum combinatorial optimization. The Vehicle Routing Problem (VRP), a core problem in logistics and transportation, is a natural application target, but it…

Emerging Technologies · Computer Science 2026-04-09 Yuan-Zheng Lei , Yaobang Gong , Xianfeng Terry Yang , Nii Attoh-Okine

The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm that finds approximate solutions to problems in combinatorial optimization, especially those that can be formulated as a Quadratic Unconstrained Binary…

Quantum Physics · Physics 2025-09-23 Bikrant Bhattacharyya , Michael Capriotti , Reuben Tate

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent quantum algorithm designed to find approximate solutions to combinatorial optimization problems, which are challenging for classical computers. In the current era, where…

Currency arbitrage capitalizes on price discrepancies in currency exchange rates between markets to produce profits with minimal risk. By employing a combinatorial optimization problem, one can ascertain optimal paths within directed…

Computational Finance · Quantitative Finance 2025-02-25 Sangram Deshpande , Elin Ranjan Das , Frank Mueller