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

2026-04-12

AI Summary

This study addresses the real-world combinatorial optimization problem of wedding banquet seating arrangements, incorporating multiple constraints such as guests’ familial relationships, shared interests, and physical needs. It presents the first formalization of this socially constrained optimization task as a Cost Function Network (CFN) benchmark instance and introduces a plugin library for the Masala platform to enable unified evaluation of both classical and quantum solvers. Experiments were conducted using Masala’s Monte Carlo CFN solver alongside the D-Wave Advantage 2 quantum annealer, employing one-hot, domain-wall, and approximate binary encoding strategies. Results demonstrate that classical methods efficiently yield optimal solutions, whereas current quantum hardware exhibits limited performance on this practical problem, highlighting significant bottlenecks in its real-world applicability.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Machine Learning: Quantum Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

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.

Problem

• wedding seating
• optimization
• constraint satisfaction
• combinatorial optimization
• real-world benchmark

Innovation

• cost function network
• quantum annealing
• combinatorial optimization
• real-world benchmark
• Monte Carlo optimization