Multi-round QAOA and advanced mixers on a trapped-ion quantum computer

Abstract

Combinatorial optimization problems on graphs have broad applications in science and engineering. The Quantum Approximate Optimization Algorithm (QAOA) is a method to solve these problems on a quantum computer by applying multiple rounds of variational circuits. However, there exist several challenges limiting the real-world applications of QAOA. In this paper, we demonstrate on a trapped-ion quantum computer that QAOA results improve with the number of rounds for multiple problems on several arbitrary graphs. We also demonstrate an advanced mixing Hamiltonian that allows sampling of all optimal solutions with predetermined weights. Our results are a step towards applying quantum algorithms to real-world problems.

8 pages, 5 figures

References in corpus (2)

• A Quantum Approximate Optimization Algorithm
• Unsupervised Machine Learning on a Hybrid Quantum Computer

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