Journal
QUANTUM SCIENCE AND TECHNOLOGY
Volume 5, Issue 3, Pages -Publisher
IOP PUBLISHING LTD
DOI: 10.1088/2058-9565/ab8c2b
Keywords
quantum algorithms; quantum computing; QAOA; tensor networks
Funding
- European Union [828826]
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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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