Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit

2019/08/23 by Michael Streif, Streif, Michael, Martin Leib +1 · 10 citations
Computer Science · Physics and Astronomy · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum-Dot Cellular Automata #quant-ph

paper · pdf · doi:10.48550/arxiv.1908.08862

arxiv created 2019/08/23 · openalex publication_date 2019/08/23 · arxiv updated 2019/08/26 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28

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 infer 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. Moreover, we show the applicability of our method beyond the scope of QAOA, in improving schedules for quantum annealing.

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