Approximating the quantum approximate optimization algorithm with digital-analog interactions
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The quantum approximate optimization algorithm was proposed as a heuristic method for solving combinatorial optimization problems on near-term quantum computers and may be among the first algorithms to perform useful computations in the postsupremacy, noisy, intermediate-scale era of quantum computing. In this work we exploit the recently proposed digital-analog quantum computation paradigm, in which the versatility of programmable universal quantum computers and the error resilience of quantum simulators are combined to improve platforms for quantum computation. We show that the digital-analog paradigm is suited to the quantum approximate optimization algorithm due to the algorithm's variational resilience against the coherent errors introduced by the scheme. By performing large-scale simulations and providing analytical bounds for its performance in devices with finite single-qubit operation time we observe regimes of single-qubit operation speed in which the considered variational algorithm provides a significant improvement over nonvariational counterparts in the digital-analog scheme.