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Quantum approximate optimization algorithm (QAOA) is a promising variational quantum algorithm for combinatorial optimization problems. However, the implementation of QAOA is limited due to the requirement that the problems be mapped to…

量子物理 · 物理学 2023-07-19 Hui-Min Li , Jin-Min Liang , Zhi-Xi Wang , Shao-Ming Fei

Controlled quantum mechanical devices provide a means of simulating more complex quantum systems exponentially faster than classical computers. Such "quantum simulators" rely heavily upon being able to prepare the ground state of…

量子物理 · 物理学 2012-08-01 Dvir Kafri , Jacob M. Taylor

The Quantum Approximate Optimization Algorithm (QAOA) is an algorithmic framework for finding approximate solutions to combinatorial optimization problems, derived from an approximation to the Quantum Adiabatic Algorithm (QAA). In solving…

量子物理 · 物理学 2020-02-05 Yue Ruan , Samuel Marsh , Xilin Xue , Xi Li , Zhihao Liu , Jingbo Wang

We report on the energy-expectation-value landscapes produced by the single-layer ($p=1$) Quantum Approximate Optimization Algorithm (QAOA) when being used to solve Ising problems. The landscapes are obtained using an analytical formula…

量子物理 · 物理学 2022-09-26 Asier Ozaeta , Wim van Dam , Peter L. McMahon

We study the Quantum Approximate Optimization Algorithm (QAOA) in the context of the Max-Cut problem. Near-term (noisy) quantum devices are only able to (accurately) execute QAOA at low circuit depths while QAOA requires a relatively high…

量子物理 · 物理学 2023-08-24 Reuben Tate , Majid Farhadi , Creston Herold , Greg Mohler , Swati Gupta

Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading…

机器学习 · 计算机科学 2022-06-16 Sami Khairy , Ruslan Shaydulin , Lukasz Cincio , Yuri Alexeev , Prasanna Balaprakash

There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for…

量子物理 · 物理学 2021-06-23 Daniel J. Egger , Jakub Marecek , Stefan Woerner

The Quantum Approximate Optimization Algorithm (QAOA) has enjoyed increasing attention in noisy intermediate-scale quantum computing due to its application to combinatorial optimization problems. Because combinatorial optimization problems…

最优化与控制 · 数学 2024-01-18 Yunsoo Ha , Sara Shashaani , Matt Menickelly

The Quantum Approximate Optimization Algorithm (QAOA) adopts a hybrid quantum-classical approach to find approximate solutions to variational optimization problems. In fact, it relies on a classical subroutine to optimize the parameters of…

量子物理 · 物理学 2023-07-31 Simone Tibaldi , Davide Vodola , Edoardo Tignone , Elisa Ercolessi

We embed 1-layer QAOA circuits into the larger class of parameterized Instantaneous Quantum Polynomial circuits to produce an improved variational quantum algorithm for solving combinatorial optimization problems. The use of analytic…

量子物理 · 物理学 2024-02-13 Sebastian Leontica , David Amaro

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…

The Quantum Approximate Optimisation Algorithm (QAOA) is a widely studied quantum-classical iterative heuristic for combinatorial optimisation. While QAOA targets problems in complexity class NP, the classical optimisation procedure…

量子物理 · 物理学 2025-11-12 Tom Krüger , Wolfgang Mauerer

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…

量子物理 · 物理学 2021-12-15 Santosh Kumar Radha

The preparation of thermal equilibrium states is important for the simulation of condensed-matter and cosmology systems using a quantum computer. We present a method to prepare such mixed states with unitary operators, and demonstrate this…

A new methodology is proposed to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). Our methodology successfully finds all optimized approximated solutions for Boolean problems,…

量子物理 · 物理学 2024-07-11 Ali Al-Bayaty , Marek Perkowski

We present a comparison of different quantum state preparation algorithms and their overall efficiency for the Schwinger model with a theta term. While adiabatic state preparation (ASP) is proved to be effective, in practice it leads to…

高能物理 - 格点 · 物理学 2024-11-04 Alexei Bazavov , Brandon Henke , Leon Hostetler , Dean Lee , Huey-Wen Lin , Giovanni Pederiva , Andrea Shindler

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…

量子物理 · 物理学 2022-08-23 Ohad Amosy , Tamuz Danzig , Ely Porat , Gal Chechik , Adi Makmal

Quantum computing promises solutions to classically difficult and new-found problems through controlling the subtleties of quantum computing. The Quantum Approximate Optimisation Algorithm (QAOA) is a recently proposed quantum algorithm…

量子物理 · 物理学 2024-12-24 Nicholas J. Pritchard

The Quantum Approximate Optimization Algorithm (QAOA) -- one of the leading algorithms for applications on intermediate-scale quantum processors -- is designed to provide approximate solutions to combinatorial optimization problems with…

量子物理 · 物理学 2024-09-18 Pontus Vikstål , Laura García-Álvarez , Shruti Puri , Giulia Ferrini

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized…

量子物理 · 物理学 2024-04-23 Ningyi Xie , Xinwei Lee , Dongsheng Cai , Yoshiyuki Saito , Nobuyoshi Asai