Over the years, advances in long-term open pit mine planning have significantly optimised ore extraction and transport to metallurgical plants. However, as additional aspects, such as uncertainty, more stages in the mineral value chain, and greater detail within them are integrated, model formulations have become increasingly complex. This complexity has heightened the demand for computational resources, often leading researchers to prioritise efficiency over precision in their proposals. The advent of quantum computing presents a promising approach to addressing these limitations, offering computational power that has shown remarkable success across diverse fields. With the growing availability of quantum computers and simulators, this work presents the basis of some preliminary findings from an ongoing Chilean project exploring quantum technology applications in the long-term mine planning. This paper introduces key concepts in quantum computing, the necessary mathematical reformulations, and a proof-of-concept implementation integrating the Ultimate Pit Limit (UPL) problem with the Quantum Approximate Optimisation Algorithm (QAOA) via two case studies: one executed on a real quantum computer and another on a quantum simulator. These case studies not only reveal current technological limitations but also provide insights into the potential of quantum approaches for addressing mine-planning challenges.