Entangled happily ever after: Wedding reception seating mapped to classical and quantum optimizers

TL;DR

This paper explores the application of classical and quantum optimization algorithms to the real-world problem of wedding seating arrangements, providing a benchmark set and code for future research.

Contribution

It introduces a novel real-world benchmark problem for optimization algorithms based on wedding seating arrangements and compares classical and quantum solver performances.

Findings

Classical Monte Carlo solvers outperformed quantum annealing on the seating problem.

The D-Wave Advantage 2 system struggled to find optimal solutions compared to classical methods.

Provides a benchmark set and plugin library for future optimization research.

Abstract

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.