AI RESEARCH
Q-BIOLAT: Binary Latent Protein Fitness Landscapes for QUBO-Based Optimization
arXiv CS.LG
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ArXi:2603.27526v1 Announce Type: new Protein fitness optimization is inherently a discrete combinatorial problem, yet most learning-based approaches rely on continuous representations and are primarily evaluated through predictive accuracy. We Beyond its formulation, Q-BIOLAT provides a representation-centric perspective on protein fitness modeling. We show that representations with similar predictive performance can induce fundamentally different optimization landscapes.