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.

蛋白质序列可以被看作自然语言处理中的句子，并且可以使用现有的量子自然语言处理框架解析为合理量子比特的参数化量子电路，这些电路可以被训练用于解决各种与蛋白质相关的机器学习问题。论文提出了适应蛋白质数据需求的详细方法，通过全面的实验结果支持，展示了两种不同的量子张量网络（QTN），并使用经典循环神经网络（RNN）和卷积神经网络（CNN）的灵感来解决所提到的二元分类任务。我们的最佳量子模型的准确率达到 94%，与使用 ESM2 蛋白质语言模型嵌入的经典模型性能相媲美，而我们最好的量子模型仅需要约 800 个参数。研究表明，这些混合模型表现出有希望的性能，展示了它们与类似复杂度的经典模型竞争的潜力。