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The paper introduces Q-BIOLAT, a framework that uses protein language model embeddings to create binary latent representations of protein sequences, enabling the approximation of protein fitness landscapes as QUBO problems. This allows for efficient combinatorial search using classical heuristics like simulated annealing and genetic algorithms to identify high-fitness protein variants. Experiments on the ProteinGym benchmark demonstrate that Q-BIOLAT captures meaningful structure in protein fitness landscapes and retrieves sequences with high fitness, paving the way for quantum-assisted protein engineering.
Quantum annealing could soon accelerate protein engineering: Q-BIOLAT formulates protein fitness as a QUBO problem, directly compatible with emerging quantum annealing hardware.
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
