# A QUBO Framework for Team Formation

arXiv: https://arxiv.org/abs/2503.23209 · HTML: https://mlsanity.com/paper/2503.23209/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 29 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Neural Combinatorial Optimization with Reinforcement Learning](https://mlsanity.com/paper/1611.09940/) (2016)
- [Combinatorial optimization and reasoning with graph neural networks](https://mlsanity.com/paper/2102.09544/) (2021)
- [Combinatorial Optimization with Physics-Inspired Graph Neural Networks](https://mlsanity.com/paper/2107.01188/) (2021)
- [Rep the Set: Neural Networks for Learning Set Representations](https://mlsanity.com/paper/1904.01962/) (2019)
- [Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications](https://mlsanity.com/paper/1904.09354/) (2019)
- [Guess Free Maximization of Submodular and Linear Sums](https://mlsanity.com/paper/1810.03813/) (2018)
- [Penalty Weights in QUBO Formulations: Permutation Problems](https://mlsanity.com/paper/2206.11040/) (2022)
- [An Efficient Framework for Balancing Submodularity and Cost](https://mlsanity.com/paper/2002.07782/) (2020)
- [Deep Neural Networks for Optimal Team Composition](https://mlsanity.com/paper/1805.03285/) (2018)
- [Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods](https://mlsanity.com/paper/2310.05309/) (2023)
- [Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks](https://mlsanity.com/paper/2310.07979/) (2023)
- [Finding teams that balance expert load and task coverage](https://mlsanity.com/paper/2011.04428/) (2020)
- [Forming Coordinated Teams that Balance Task Coverage and Expert Workload](https://mlsanity.com/paper/2503.05898/) (2025)

## Abstract (arXiv)

The team formation problem assumes a set of experts and a task, where each expert has a set of skills and the task requires some skills. The objective is to find a set of experts that maximizes coverage of the required skills while simultaneously minimizing the costs associated with the experts. Different definitions of cost have traditionally led to distinct problem formulations and algorithmic solutions. We introduce the unified TeamFormation formulation that captures all cost definitions for team formation problems that balance task coverage and expert cost. Specifically, we formulate three TeamFormation variants with different cost functions using quadratic unconstrained binary optimization (QUBO), and we evaluate two distinct general-purpose solution methods. We show that solutions based on the QUBO formulations of TeamFormation problems are at least as good as those produced by established baselines. Furthermore, we show that QUBO-based solutions leveraging graph neural networks can effectively learn representations of experts and skills to enable transfer learning, allowing node embeddings from one problem instance to be efficiently applied to another.