Exploiting adiabatic quantum computing in deep space missions

Several fundamental questions demand space exploration, posing tremendous computational tasks to address modelling and data processing. Quantum computing has been proposed to address deep space-related problems from quantum gravity, nuclear processes and many-body field theory simulation to artificial intelligence and related space mission planning. Adiabatic quantum computers have been proposed for solving optimization problems, such as space mission scheduling. The problem of assigning jobs to resources in a scientific mission, while satisfying certain constraints, can be tackled with the help of D-Wave's quantum annealer, which exploits the adiabatic theorem to find the minimum energy solutions of a given system, encoded as the Hamiltonian of a quadratic unconstrained binary optimization (QUBO) problem. In its simplest em-bodiement, the scheduling can be implemented as a graph coloring (GC) problem, where the goal is to assign different colors (out of a given set) to connected nodes of an input graph, while respecting given constraints. The feasibility of our methods and the performance of quantum annealers are studied on randomly generated GC instances. A second framework studied is the job shop scheduling problem (JSP) that encodes more real-like scheduling problems of interest to agencies and industries. By implementing the problems with a QUBO formulation, we are able to find the optimal solution to three JSP instances present in the literature. The biggest one is solved with a quantum computation approach here for the first time.