Promise of Graph Sparsification and Decomposition for Noise Reduction in QAOA: Analysis for Trapped-Ion Compilations
Jai Moondra, Philip C. Lotshaw, Greg Mohler, Swati Gupta

TL;DR
This paper introduces provable graph sparsification and decomposition techniques to improve noise resilience and reduce circuit complexity in QAOA implementations, especially for trapped-ion quantum simulators.
Contribution
It provides the first theoretical guarantees that sparsification and decomposition can enhance quantum noise resilience and circuit efficiency in QAOA compilation.
Findings
Reduction of $H_\text{Ising}$ pulses from $O(n^2)$ to $O(n\log(n/\epsilon))$
Decrease of Pauli-$X$ bit flips from $O(n^2)$ to $O(n\log(n/\epsilon)/\epsilon^2)$
Demonstrated noise reduction through theory and numerical simulations.
Abstract
We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem. Our main focus is on compilation with trapped-ion simulators using Pauli- operations and all-to-all Ising Hamiltonian evolution generated by Molmer-Sorensen or optical dipole force interactions, though some of our results also apply to standard gate-based compilations. Our results are based on principles of graph sparsification and decomposition; the former reduces the number of edges in a graph while maintaining its cut structure, while the latter breaks a weighted graph into a small number of unweighted graphs. Though these techniques have been used as heuristics in various hybrid quantum algorithms, there have been no guarantees on their performance, to the best of our knowledge.…
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