BHT-QAOA: The Generalization of Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians [PDF]
EntropyA new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the “Boolean-Hamiltonians Transform for QAOA” (BHT-QAOA). Because a great deal of
Ali Al-Bayaty, Marek Perkowski
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Performance analysis of multi-angle QAOA for $$p > 1$$ p > 1 [PDF]
Scientific ReportsIn this paper we consider the scalability of multi-angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets.
Igor Gaidai, Rebekah Herrman
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Systematic study on the dependence of the warm-start quantum approximate optimization algorithm on approximate solutions [PDF]
Scientific ReportsQuantum approximate optimization algorithm (QAOA) is a promising hybrid quantum-classical algorithm to solve combinatorial optimization problems in the era of noisy intermediate-scale quantum computers.
Ken N. Okada +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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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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Exploiting Symmetry Reduces the Cost of Training QAOA
IEEE Transactions on Quantum Engineering, 2021 A promising approach to the practical application of the quantum approximate optimization algorithm (QAOA) is finding QAOA parameters classically in simulation and sampling the solutions from QAOA with optimized parameters on a quantum computer. Doing so
Ruslan Shaydulin, Stefan M. Wild
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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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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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