2019/08/23 by Michael Streif, Streif, Michael, Martin Leib +1 · 4 citations
Computer Science · #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph) #Quantum-Dot Cellular Automata
paper · pdf · doi:10.48550/arxiv.1908.08862
openalex publication_date 2019/08/23 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
In this paper, we eliminate the classical outer learning loop of the Quantum\nApproximate Optimization Algorithm (QAOA) and present a strategy to find good\nparameters for QAOA based on topological arguments of the problem graph and\ntensor network techniques. Starting from the observation of the concentration\nof control parameters of QAOA, we find a way to classically infer parameters\nwhich scales polynomially in the number of qubits and exponentially with the\ndepth of the circuit. Using this strategy, the quantum processing unit (QPU) is\nonly needed to infer the final state of QAOA. This method paves the way for a\nvariation-free version of QAOA and makes QAOA more practical for applications\non NISQ devices. Moreover, we show the applicability of our method beyond the\nscope of QAOA, in improving schedules for quantum annealing.\n