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Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm, which it can approximate with sufficient depth. However, it is unclear to what extent the lessons from the adiabatic regime apply to QAOA as…

The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a…

Quantum Physics · Physics 2020-04-28 Li Li , Minjie Fan , Marc Coram , Patrick Riley , Stefan Leichenauer

The present tutorial aims to provide a comprehensible and easily accessible introduction into the theory and implementation of the famous Quantum Approximate Optimization Algorithm (QAOA). We lay our focus on practical aspects and…

Quantum Physics · Physics 2023-01-24 Andreas Sturm

Preparing the ground state of a given Hamiltonian and estimating its ground energy are important but computationally hard tasks. However, given some additional information, these problems can be solved efficiently on a quantum computer. We…

Quantum Physics · Physics 2020-12-16 Lin Lin , Yu Tong

A frequent starting point of quantum computation platforms are two-state quantum systems, i.e., qubits. However, in the context of integer optimization problems, relevant to scheduling optimization and operations research, it is often more…

Finding the ground state of a Hamiltonian system is of great significance in many-body quantum physics and quantum chemistry. We propose an improved iterative quantum algorithm to prepare the ground state of a Hamiltonian. The crucial point…

Quantum Physics · Physics 2022-10-25 Jin-Min Liang , Qiao-Qiao Lv , Shu-Qian Shen , Ming Li , Zhi-Xi Wang , Shao-Ming Fei

Constrained combinatorial optimization remains challenging for quantum algorithms because feasibility must be explicitly enforced, typically through penalty terms or problem-specific mixers. We introduce Partitioned-Constraint QAOA…

Quantum Physics · Physics 2026-05-20 Anthony Wilkie , Alexander DeLise , Andrew Del Real , Rebekah Herrman , James Ostrowski

Protein folding -- the problem of predicting the spatial structure of a protein given its sequence of amino-acids -- has attracted considerable research effort in biochemistry in recent decades. In this work, we explore the potential of…

Quantum Physics · Physics 2022-04-06 Sami Boulebnane , Xavier Lucas , Agnes Meyder , Stanislaw Adaszewski , Ashley Montanaro

The quantum approximate optimization algorithm (QAOA) is a leading iterative variational quantum algorithm for heuristically solving combinatorial optimization problems. A large portion of the computational effort in QAOA is spent by the…

Quantum Physics · Physics 2022-08-23 Ohad Amosy , Tamuz Danzig , Ely Porat , Gal Chechik , Adi Makmal

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…

Quantum Physics · Physics 2019-07-24 Mahabubul Alam , Abdullah Ash-Saki , Swaroop Ghosh

The quantum approximate optimisation ansatz (QAOA) is one of the flagship algorithms used to tackle combinatorial optimisation on graphs problems using a quantum computer, and is considered a strong candidate for early fault-tolerant…

Quantum Physics · Physics 2025-06-24 Yann Beaujeault-Taudière

The quantum approximate optimization algorithm (QAOA) is one of the most prominent proposed applications for near-term quantum computing. Here we study the ability of QAOA to solve hard constraint satisfaction problems, as opposed to…

Quantum Physics · Physics 2022-08-16 Sami Boulebnane , Ashley Montanaro

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 paper we show quantum fluctuation effect of fully frustrated Ising spin systems. Quantum annealing has been expected to be an efficient method to find ground state of optimization problems. However it is not clear when to use the…

Disordered Systems and Neural Networks · Physics 2011-06-06 Shu Tanaka

Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is,…

Quantum Physics · Physics 2024-02-07 Yash J. Patel , Sofiene Jerbi , Thomas Bäck , Vedran Dunjko

Computing many-body ground state energies and resolving electronic structure calculations are fundamental problems for fields such as quantum chemistry or condensed matter. Several quantum computing algorithms that address these problems…

Quantum Physics · Physics 2023-01-12 Karen J. Morenz Korol , Kenny Choo , Antonio Mezzacapo

A pivotal task for quantum computing is to speed up solving problems that are both classically intractable and practically valuable. Among these, combinatorial optimization problems have attracted tremendous attention due to their broad…

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

The quantum approximate optimization algorithm (QAOA) conceived for solving combinatorial optimization problems has attracted significant interest since it can be run on the existing noisy intermediate-scale quantum (NISQ) devices. A…

Information Theory · Computer Science 2022-04-12 Jingjing Cui , Gui Lu Long , Lajos Hanzo

The Quantum Approximate Optimization Algorithm (QAOA) is a promising algorithm for solving combinatorial optimization problems (COPs), with performance governed by variational parameters $\{\gamma_i, \beta_i\}_{i=0}^{p-1}$. While most prior…

Quantum Physics · Physics 2025-08-07 J. A. Montanez-Barrera , Kristel Michielsen
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