Exploring Entanglement and Parameter Sensitivity in QAOA through Quantum Fisher Information

TL;DR

This paper uses Quantum Fisher Information to analyze entanglement and parameter sensitivity in QAOA, proposing a QFI-based heuristic that improves optimization performance on Max-Cut problems.

Contribution

It provides a systematic QFI analysis of QAOA, revealing how entanglement affects sensitivity and introducing a QFI-informed heuristic for better parameter tuning.

Findings

Complete graphs have higher QFI eigenvalues than cyclic graphs.

Several QAOA settings exceed the linear QFI bound but not the Heisenberg limit.

QFI-informed heuristic improves mean energy and reduces variance in optimization.

Abstract

Quantum Fisher Information (QFI) can be used to quantify how sensitive a quantum state reacts to changes in its variational parameters, making it a natural diagnostic for algorithms such as the Quantum Approximate Optimization Algorithm (QAOA). We perform a systematic QFI analysis of QAOA for Max-Cut on cyclic and complete graphs with $N = 4 - 10$ qubits. Two mixer families are studied, RX-only and hybrid RX-RY, with depths $p = 2, 4, 6$ and $p = 3, 6, 9$, respectively, and with up to three entanglement stages implemented through cyclic- or complete-entangling patterns. Complete graphs consistently yield larger QFI eigenvalues than cyclic graphs; none of the settings reaches the Heisenberg limit ($4N^2$), but several exceed the linear bound ($4N$). Introducing entanglement primarily redistributes QFI from diagonal to off-diagonal entries: non-entangled circuits maximize per-parameter (diagonal) sensitivity, whereas entangling layers increase the covariance fraction and thus cross-parameter correlations, with diminishing returns beyond the first stage. Leveraging these observations, we propose, as a proof of concept, a QFI-Informed Mutation (QIm) heuristic that sets mutation probabilities and step sizes from the normalized diagonal QFI. On 7- and 10-qubit instances, QIm attains higher mean energies and lower variance than equal-probability and random-restart baselines over 100 runs, underscoring QFI as a lightweight, problem-aware preconditioner for QAOA and other variational quantum algorithms.