Machine-Learning-Assisted Parameterization of Quantum Walk Algorithms

Quantum walk algorithms constitute a central primitive in quantum computation, yet their practical performance often depends sensitively on the choice of execution parameters such as walk depth and initial state. Analytical parameter choices are typically conservative and instance-agnostic, which can lead to suboptimal behavior on specific problem instances. In this work, we propose a general framework for machine-learning-assisted parameterization of quantum walk algorithms, in which classical learning is used to adapt algorithmic parameters without modifying the underlying quantum decision logic. The machine learning component op erates exclusively as a classical control layer, preserving correctness guarantees while improving average-case performance. We demonstrate the framework through an application to the s–t connectivity problem, a canonical benchmark in graph algorithms and complexity theory. Classical simulations of discrete-time quantum walks on randomly generated sparse graphs show that ML-assisted pa rameter selection substantially improves success probability compared to fixed-parameter base lines, yielding multiplicative gains in average performance. These improvements are achieved despite only coarse prediction accuracy, highlighting the robustness of the approach. The results suggest a principled role for machine learning as an adaptive optimization layer in quantum algorithms, offering a practical path toward hybrid quantum–classical methods that enhance performance while maintaining theoretical soundness