Entangled happily ever after: Wedding reception seating mapped to classical and quantum optimizers
Provides a real-world benchmark for classical and quantum optimizers, but results show quantum methods underperform classical ones on this problem.
The authors mapped wedding reception seating optimization to cost function networks (CFNs) and compared classical Monte Carlo solvers with quantum annealing-based algorithms. Classical methods easily found optimal arrangements, while the D-Wave Advantage 2 system struggled.
Although optimization is one of the most promising applications of quantum computers, the development of effective optimization strategies requires real-world test cases. When planning our recent wedding reception, we realized that the problem of optimally seating our guests, given constraints related to guests' relatedness, shared interests, and physical needs, could be mapped to a cost function network (CFN) form solvable with classical or quantum optimization algorithms. We compared the seating optimization performance of classical Monte Carlo CFN solvers in the Masala software suite to that of quantum annealing-based CFN optimization algorithms using one-hot, domain-wall, and approximate binary mappings, which we had developed for protein design problems. Surprisingly, the D-Wave Advantage 2 system, which performs well on similarly-structured CFN problems for protein design, struggled to return optimal seating arrangements that were easily found by classical Monte Carlo methods. We provide our seating optimization benchmark set, and code to convert seating optimization problems to CFN problems, as a plugin library for Masala, permitting this class of real-world problems to be used to benchmark performance of current and future classical CFN solvers, quantum optimization algorithms, and quantum computing hardware.