Quantum-assisted open-pit optimization

With the recent advances in experimental realization of multi-qubit systems the idea of delegating certain real-life optimization tasks to a quantum computer becomes viable. In particular, we herein examine a variational quantum algorithm applied to a three-dimensional problem of open-pit mining, where the core of the classical optimization loop is provided by the probabilistic tensor sampling method. The developed technique is challenged against conventional optimization routines subjected to the essential optimization issues in variational quantum algorithms such as barren plateaus and multiple local minima. We demonstrate that the probabilistic tensor train-based approach allows one to steadily identify the ground state of a given system.