On the Instance Dependence of Optimal Parameters for the Quantum Approximate Optimisation Algorithm: Insights via Instance Space Analysis (2401.08142v2)

Abstract: The performance of the Quantum Approximate Optimisation Algorithm (QAOA) relies on the setting of optimal parameters in each layer of the circuit. This is no trivial task, and much literature has focused on the challenge of finding optimal parameters when the landscape is plagued with problems such as "barren plateaus". There are many choices of optimisation heuristics that can be used to search for optimal parameters, each with its own parameters and initialisation choices that affect performance. More recently, the question of whether such optimal parameter search is even necessary has been posed, with some studies showing that optimal parameters tend to be concentrated on certain values for specific types of problem instances. However, these existing studies have only examined specific instance classes of MaxCut, so it is uncertain if the claims of instance independence apply to a diverse range of instances. In this paper, we use Instance Space Analysis to study the dependence of instance characteristics on the performance of QAOA. Focusing on the MaxCut problem, we assess the effectiveness of parameter initialisation strategies and introduce a new initialisation approach based on instance characteristics called Quantum Instance-Based Parameter Initialisation (QIBPI). This study reveals that using insights about instance characteristics in choosing initialisation parameters can improve QAOA performance. We also show that, within certain instance classes, parameters from smaller instances can be transferred to larger ones. This research provides a foundation for further instance space analysis for quantum algorithms and encourages a broader class of instances to be considered to ensure conclusions are not limited to particular well-studied test problems or classes.