  We show that protein sequences can be thought of as sentences in natural
language processing and can be parsed using the existing Quantum Natural
Language framework into parameterized quantum circuits of reasonable qubits,
which can be trained to solve various protein-related machine-learning
problems. We classify proteins based on their subcellular locations, a pivotal
task in bioinformatics that is key to understanding biological processes and
disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we
demonstrate that Quantum Tensor Networks (QTN) can effectively handle the
complexity and diversity of protein sequences. We present a detailed
methodology that adapts QTN architectures to the nuanced requirements of
protein data, supported by comprehensive experimental results. We demonstrate
two distinct QTNs, inspired by classical recurrent neural networks (RNN) and
convolutional neural networks (CNN), to solve the binary classification task
mentioned above. Our top-performing quantum model has achieved a 94% accuracy
rate, which is comparable to the performance of a classical model that uses the
ESM2 protein language model embeddings. It's noteworthy that the ESM2 model is
extremely large, containing 8 million parameters in its smallest configuration,
whereas our best quantum model requires only around 800 parameters. We
demonstrate that these hybrid models exhibit promising performance, showcasing
their potential to compete with classical models of similar complexity.
