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

TL;DR

This paper proposes a classical method to determine QAOA parameters without iterative quantum-classical feedback, enhancing practicality for NISQ devices and extending to quantum annealing schedules.

Contribution

It introduces a topological and tensor network-based approach to find QAOA parameters classically, removing the need for a quantum processing unit during parameter optimization.

Findings

Classical inference of QAOA parameters scales polynomially with qubits.

Method enables a variation-free, practical QAOA implementation.

Applicable to optimizing schedules in quantum annealing.

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.