Results 11 to 20 of about 3,959 (191)
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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The effect of classical optimizers and Ansatz depth on QAOA performance in noisy devices [PDF]
Scientific ReportsThe Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) providing approximate solutions for combinatorial optimization problems.
Aidan Pellow-Jarman +5 more
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Counterdiabaticity and the quantum approximate optimization algorithm [PDF]
Quantum, 2022 The quantum approximate optimization algorithm (QAOA) is a near-term hybrid algorithm intended to solve combinatorial optimization problems, such as MaxCut. QAOA can be made to mimic an adiabatic schedule, and in the $p\to\infty$ limit the final state is
Jonathan Wurtz, Peter J. Love
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Warm-Started QAOA with Custom Mixers Provably Converges and Computationally Beats Goemans-Williamson's Max-Cut at Low Circuit Depths [PDF]
Quantum, 2023 We generalize the Quantum Approximate Optimization Algorithm (QAOA) of Farhi et al. (2014) to allow for arbitrary separable initial states with corresponding mixers such that the starting state is the most excited state of the mixing Hamiltonian.
Reuben Tate +4 more
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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 Dahl
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Quantum annealing initialization of the quantum approximate optimization algorithm [PDF]
Quantum, 2021 The quantum approximate optimization algorithm (QAOA) is a prospective near-term quantum algorithm due to its modest circuit depth and promising benchmarks.
Stefan H. Sack, Maksym Serbyn
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Feature Selection for Classification with QAOA
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.
Turati G. +2 more
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Local classical MAX-CUT algorithm outperforms $p=2$ QAOA on high-girth regular graphs [PDF]
Quantum, 2021 The $p$-stage Quantum Approximate Optimization Algorithm (QAOA$_p$) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum (NISQ) devices, but its theoretical behavior is not well understood beyond $p=1$.
Kunal Marwaha
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New Journal of Physics, 2023 The variational preparation of complex quantum states using the quantum approximate optimization algorithm (QAOA) is of fundamental interest, and becomes a promising application of quantum computers.
Zheng-Hang Sun +3 more
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Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware [PDF]
Quantum, 2022 Quantum computers may provide good solutions to combinatorial optimization problems by leveraging the Quantum Approximate Optimization Algorithm (QAOA). The QAOA is often presented as an algorithm for noisy hardware.
Johannes Weidenfeller +6 more
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