A new approach to hypergraph partitioning, developed by Cameron Ibrahim at the United States Naval Academy and colleagues from Los Alamos National Laboratory and University of Delaware, treats the problem as requiring a probability distribution over potential solutions rather than a single optimal outcome. This distributional perspective, inspired by objectives like Fair Cut Cover, aligns with the natural output of the Quantum Approximate Optimisation Algorithm (QAOA). They have created QAOA-based solvers capable of natively representing these distributional solutions and introduced a new problem, the Greatest Expected Imbalance, to illustrate the formulation’s utility. Experiments on both real-world and synthetic hypergraphs reveal that low-depth multi-angle QAOA can surpass classical approximation algorithms based on semidefinite programming, suggesting a potential advantage for quantum algorithms in tackling optimisation problems demanding distributional solutions. Quantum optimisation enhances hypergraph partitioning performance sharply Low-depth multi-angle QAOA outperforms classical approximation algorithms, achieving a 7-10% improvement in objective function values on real-world and synthetic hypergraphs. This surpasses the limitations of previous methods reliant on semidefinite programming and hyperplane rounding, which struggled to find solutions with comparable quality, particularly for larger, more complex hypergraphs. The research introduces a new approach to hypergraph partitioning by framing it as a problem requiring a probability distribution over potential solutions, rather than a single optimal outcome, enabling the native representation of distributional solutions via quantum algorithms. Further analysis showed that low-depth multi-angle QAOA could outperform classical approximation algorithms based on semidefinite programming on proposed objectives, highlighting potential benefits for optimisation problems where the solution is a distribution rather than a single partition. For the Greatest Expected Imbalance problem, QAOA natively represented distributional solutions through quantum states, aligning with objectives such as Fair Cut Cover. These formulations connect balanced hypergraph partitioning, polarized community discovery, and distributional fairness within a unified quantum optimisation framework. Experiments utilising real-world and