Inference of maximum parsimony phylogenetic trees with model-based classical and quantum methods

TL;DR

This paper introduces new optimization models for maximum parsimony phylogenetic tree reconstruction suitable for classical and quantum computing, demonstrating improved solutions and potential for quantum advantage.

Contribution

It presents a novel branch-based modeling approach that reduces complexity and is validated with classical and quantum solvers, advancing phylogenetic inference methods.

Findings

Classical solver outperforms heuristics on the GAPDH dataset.

Quantum simulations find exact solutions for small instances with rapid convergence.

The branch-based model reduces variables and constraints, improving efficiency.

Abstract

The maximum parsimony phylogenetic tree reconstruction problem is NP-hard, presenting a computational bottleneck for classical computing and motivating the exploration of emerging paradigms like quantum computing. To this end, we design three optimization models compatible with both classical and quantum solvers. Our method directly searches the complete solution space of all possible tree topologies and ancestral states, thereby avoiding the potential biases associated with pre-constructing candidate internal nodes. Among these models, the branch-based model drastically reduces the number of variables and explicit constraints through a specific variable definition, providing a novel modeling approach effective not only for phylogenetic tree building but also for other tree problems. The correctness of this model is validated with a classical solver, which obtains solutions that are generally better than those from heuristics on the GAPDH gene dataset. Moreover, our quantum simulations successfully find the exact optimal solutions for small-scale instances with rapid convergence, highlighting the potential of quantum computing to offer a new avenue for solving these intractable problems in evolutionary biology.