Clifford Accelerated Adaptive QAOA

TL;DR

This paper introduces Clifford-based preoptimization and approximation techniques to enhance adaptive QAOA performance, enabling more efficient gate selection and convergence improvements in quantum algorithms for MaxCut and TFIM problems.

Contribution

It presents a novel Clifford Point approximation method integrated with ADAPT-QAOA, improving initialization, gate selection, and convergence in quantum optimization.

Findings

Clifford Point preoptimization improves gate selection in ADAPT-QAOA.

Clifford approximations enable more parallel and classical operator selection.

Low-rank T-gate approximation enhances convergence in MaxCut and TFIM problems.

Abstract

Clifford Circuit Initializaton improves on initial guess of parameters on Parametric Quantum Circuits (PQCs) by leveraging efficient simulation of circuits made out of gates from the Clifford Group. The parameter space is pre-optimized by exploring the Hilbert space in a reduced ensemble of Clifford-expressible points (Clifford Points), providing better initialization. Simultaneously, dynamical circuit reconfiguration algorithms, such as ADAPT-QAOA, improve on QAOA performances by providing a gate re-configuration routine while the optimization is being executed. In this article, we show that Clifford Point approximations at multiple levels of ADAPT allow for multiple improvements while increasing quantum-classical integration opportunities. First we show numerically that Clifford Point preoptimization offers non-trivial gate-selection behavior in ADAPT with some possible convergence improvement. Second, that Clifford Point approximations allows for more suited, fully parallel and fully classical ADAPT operator selection for MaxCut and the TFIM problem. Finally, we show that applying 10 to 30\% error approximation on T-gates using low-rank stabilizer decomposition can provide significative improvements in convergence quality for the MaxCut and TFIM problem. The latter hints at significant T-gate over-representation in antsatz design, opening opportunities for aggressive compilation optimizations.