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arXiv · 2204.13432

Iterative Quantum Optimization with Adaptive Problem Hamiltonian

Yifeng Rocky Zhu·David Joseph·Cong Ling·Florian Mintert
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Abstract

Quantum optimization algorithms hold the promise of solving classically hard, discrete optimization problems in practice. The requirement of encoding such problems in a Hamiltonian realized with a finite -- and currently small -- number of qubits, however, poses the risk of finding only the optimum within the restricted space supported by this Hamiltonian. We describe an iterative algorithm in which a solution obtained with such a restricted problem Hamiltonian is used to define a new problem Hamiltonian that is better suited than the previous one. In numerical examples of the shortest vector problem, we show that the algorithm with a sequence of improved problem Hamiltonians converges to the desired solution.

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Yifeng Rocky Zhu, David Joseph, Cong Ling, Florian Mintert. 2022-04-28. Iterative Quantum Optimization with Adaptive Problem Hamiltonian. https://doi.org/10.1103/physreva.106.022435

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