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Quantum Alternating Operator Ansatz (QAOA) Phase Diagrams and Applications for Quantum Chemistry

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arxiv 2108.13056 v2 pith:EOS2MIEU submitted 2021-08-30 quant-ph cond-mat.mtrl-scicond-mat.str-el

classification quant-phcond-mat.mtrl-scicond-mat.str-el
keywords qaoaquantumadiabaticapplicationsapplyapproachesbehaviorbeyond
verification ladder T0 review T1 audit T2 compute T3 formal
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Determining Hamiltonian ground states and energies is a challenging task with many possible approaches on quantum computers. While variational quantum eigensolvers are popular approaches for near term hardware, adiabatic state preparation is an alternative that does not require noisy optimization of parameters. Beyond adiabatic schedules, QAOA is an important method for optimization problems. In this work we modify QAOA to apply to finding ground states of molecules and empirically evaluate the modified algorithm on several molecules. This modification applies physical insights used in classical approximations to construct suitable QAOA operators and initial state. We find robust qualitative behavior for QAOA as a function of the number of steps and size of the parameters, and demonstrate this behavior also occurs in standard QAOA applied to combinatorial search. To this end we introduce QAOA phase diagrams that capture its performance and properties in various limits. In particular we show a region in which non-adiabatic schedules perform better than the adiabatic limit while employing lower quantum circuit depth. We further provide evidence our results and insights also apply to QAOA applications beyond chemistry.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 4 citations worldwide. Full citation record

  1. Evaluating the performance of quantum processing units at large width and depth

    quant-ph 2025-02 conditional novelty 6.0 of 10

    A cross-vendor benchmark using fixed-parameter QAOA ranks 24 quantum processors by how well they preserve algorithmic signal at extreme circuit depth and width.

  2. Non-Variational Quantum Random Access Optimization with Alternating Operator Ansatz

    quant-ph 2025-02 conditional novelty 6.0 of 10

    Non-variational QAOA with fixed angles solves QRAO's relaxed MaxCut Hamiltonian with performance close to optimized parameters and about three times fewer qubits than standard QAOA.

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