Training the quantum approximate optimization algorithm without access to a quantum processing unit

Streif M, Leib M (2020)


Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 5

Journal Issue: 3

DOI: 10.1088/2058-9565/ab8c2b

Abstract

In this paper, we eliminate the classical outer learning loop of the quantum approximate optimization algorithm (QAOA) and present a strategy to find good parameters for QAOA based on topological arguments of the problem graph and tensor network techniques. Starting from the observation of the concentration of control parameters of QAOA, we find a way to classically infer parameters which scales polynomially in the number of qubits and exponentially with the depth of the circuit. Using this strategy, the quantum processing unit (QPU) is only needed to sample from the final state of QAOA. This method paves the way for a variation-free version of QAOA and makes QAOA more practical for applications on NISQ devices. We investigate the performance of the proposed approach for the initial assumptions and its resilience with respect to situations where they are not fulfilled. Moreover, we investigate the applicability of our method beyond the scope of QAOA, in improving schedules for quantum annealing.

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How to cite

APA:

Streif, M., & Leib, M. (2020). Training the quantum approximate optimization algorithm without access to a quantum processing unit. Quantum Science and Technology, 5(3). https://dx.doi.org/10.1088/2058-9565/ab8c2b

MLA:

Streif, Michael, and Martin Leib. "Training the quantum approximate optimization algorithm without access to a quantum processing unit." Quantum Science and Technology 5.3 (2020).

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