A loop Quantum Approximate Optimization Algorithm with Hamiltonian updating

• URL: http://arxiv.org/abs/2109.11350v2
• Date: Fri, 24 Sep 2021 01:57:27 GMT
• Title: A loop Quantum Approximate Optimization Algorithm with Hamiltonian updating
• Authors: Fang-Gang Duan and Dan-Bo Zhang
• Abstract summary: We propose a quantum approximate optimization algorithm(QAOA) with a very shallow circuit, called loop-QAOA, to avoid issues of noises at intermediate depths. The insight of exploiting outputs from shallow circuits as bias may be applied for other quantum algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing noisy-resilience quantum algorithms is indispensable for practical applications on Noisy Intermediate-Scale Quantum~(NISQ) devices. Here we propose a quantum approximate optimization algorithm~(QAOA) with a very shallow circuit, called loop-QAOA, to avoid issues of noises at intermediate depths, while still can be able to exploit the power of quantum computing. The key point is to use outputs of shallow-circuit QAOA as a bias to update the problem Hamiltonian that encodes the solution as the ground state. By iterating a loop between updating the problem Hamiltonian and optimizing the parameterized quantum circuit, the loop-QAOA can gradually transform the problem Hamiltonian to one easy for solving. We demonstrate the loop-QAOA on Max-Cut problems both with and without noises. Compared with the conventional QAOA whose performance will decrease due to noises, the performance of the loop-QAOA can still get better with an increase in the number of loops. The insight of exploiting outputs from shallow circuits as bias may be applied for other quantum algorithms.

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