Approximating the quantum approximate optimization algorithm with digital-analog interactions

Abstract

The quantum approximate optimisation algorithm was proposed as a heuristic method for solving combinatorial optimisation problems on near-term quantum computers and may be among the first algorithms to perform useful computations in the post-supremacy, 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 variational quantum approximate optimisation algorithm, due to its inherent resilience against coherent errors, by performing large-scale simulations and providing analytical bounds for its performance in devices with finite single-qubit operation times. We observe regimes of single-qubit operation speed in which the considered variational algorithm provides a significant improvement over non-variational counterparts.

14 pages, 8 figures

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• Digital-analog quantum simulation of fermionic models
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• Digital-analog quantum genetic algorithm using Rydberg-atom arrays
• Digital-Analog Counterdiabatic Quantum Optimization with Trapped Ions
• Enhancing Quantum Annealing in Digital-Analog Quantum Computing
• Benchmarking Digital-Analog Quantum Computation
• Digital-Analog Quantum Machine Learning
• Programming tools for Analogue Quantum Computing in the High-Performance Computing Context -- A Review
• A Useful Metric for the NISQ Era: Qubit Error Probability and Its Role in Zero Noise Extrapolation