Results 11 to 20 of about 4,386 (222)
QuantumDespite its popularity, several empirical and theoretical studies suggest that the quantum approximate optimization algorithm (QAOA) has persistent issues in providing a substantial practical advantage.
Gereon Koßmann +4 more
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QAOA of the Highest Order [PDF]
2022 IEEE 19th International Conference on Software Architecture Companion (ICSA-C), 2022 The Quantum Approximate Optimization Algorithm (QAOA) has been one of the leading candidates for near-term quantum advantage in gate-model quantum computers. From its inception, this algorithm has sparked the desire for comparison between gate-model and annealing platforms.
Colin Campbell, Edward D. Dahl
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Benchmarking Metaheuristic-Integrated QAOA against Quantum Annealing [PDF]
, 2023 The Quantum Approximate Optimization Algorithm (QAOA) is one of the most promising Noisy Intermediate Quantum Algorithms (NISQ) in solving combinatorial optimizations and displays potential over classical heuristic techniques. Unfortunately, QAOA performance depends on the choice of parameters and standard optimizers often fail to identify key ...
Arul Rhik Mazumder +2 more
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Elementary proof of QAOA convergence
New Journal of PhysicsThe quantum alternating operator ansatz (QAOA) and its predecessor, the quantum approximate optimization algorithm, are one of the most widely used quantum algorithms for solving combinatorial optimization problems.
Lennart Binkowski +3 more
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Pitfalls of the Sublinear QAOA-Based Factorization Algorithm [PDF]
IEEE Access, 2023 Quantum computing devices are believed to be powerful in solving the prime factorization problem, which is at the heart of widely deployed public-key cryptographic tools. However, the implementation of Shor's quantum factorization algorithm requires significant resources scaling linearly with the number size; taking into account an overhead that is ...
Sergey V. Grebnev +5 more
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Feature Selection for Classification with QAOA [PDF]
2022 IEEE International Conference on Quantum Computing and Engineering (QCE), 2022 Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models.
Gloria Turati +2 more
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Hybrid Classical-Quantum Simulation of MaxCut using QAOA-in-QAOA [PDF]
2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)The Quantum approximate optimization algorithm (QAOA) is a leading hybrid classical-quantum algorithm for solving complex combinatorial optimization problems. QAOA-in-QAOA (QAOA^2) uses a divide-and-conquer heuristic to solve large-scale Maximum Cut (MaxCut) problems, where many subgraph problems can be solved in parallel.
Aniello Esposito, Tamuz Danzig
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QAOA-in-QAOA: Solving Large-Scale MaxCut Problems on Small Quantum Machines
Physical Review Applied, 2023 The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimization algorithms (QAOAs), which utilize the power of quantum machines and inherit the spirit of adiabatic evolution, are novel approaches to tackle combinatorial problems with ...
Zeqiao Zhou +3 more
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Impact of graph structures for QAOA on MaxCut [PDF]
Quantum Information Processing, 2021 The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has been studied extensively on specific families of graphs, however, little is known about the algorithm on arbitrary graphs.
Rebekah Herrman +5 more
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Compiler Optimizations for QAOA [PDF]
Proceedings of the 43rd IEEE/ACM International Conference on Computer-Aided DesignYuchen Zhu +6 more
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