Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization

TL;DR

Q-BIOLAT introduces a novel framework that models protein fitness landscapes in binary latent spaces using pretrained language models and QUBO formulations, enabling efficient optimization and potential quantum hardware integration.

Contribution

It presents a new method combining protein embeddings, binary latent representations, and QUBO models for protein fitness optimization, bridging protein learning and quantum annealing.

Findings

Q-BIOLAT effectively captures protein fitness landscape structure.

The method reliably identifies high-fitness variants.

Different optimization strategies show distinct strengths in various latent space dimensions.

Abstract

We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into compact binary latent representations. In this space, protein fitness is approximated using a quadratic unconstrained binary optimization (QUBO) model, enabling efficient combinatorial search via classical heuristics such as simulated annealing and genetic algorithms. On the ProteinGym benchmark, we demonstrate that Q-BIOLAT captures meaningful structure in protein fitness landscapes and enables the identification of high-fitness variants. Despite using a simple binarization scheme, our method consistently retrieves sequences whose nearest neighbors lie within the top fraction of the training fitness distribution, particularly under the strongest configurations. We further show that different optimization strategies exhibit distinct behaviors, with evolutionary search performing better in higher-dimensional latent spaces and local search remaining competitive in preserving realistic sequences. Beyond its empirical performance, Q-BIOLAT provides a natural bridge between protein representation learning and combinatorial optimization. By formulating protein fitness as a QUBO problem, our framework is directly compatible with emerging quantum annealing hardware, opening new directions for quantum-assisted protein engineering. Our implementation is publicly available at: https://github.com/HySonLab/Q-BIOLAT