Application of Quantum Tensor Networks for Protein Classification

• URL: http://arxiv.org/abs/2403.06890v1
• Date: Mon, 11 Mar 2024 16:47:09 GMT
• Title: Application of Quantum Tensor Networks for Protein Classification
• Authors: Debarshi Kundu, Archisman Ghosh, Srinivasan Ekambaram, Jian Wang, Nikolay Dokholyan, Swaroop Ghosh
• Abstract summary: We show that protein sequences can be thought of as sentences in natural language processing. We classify proteins based on their subcellular locations. We demonstrate that Quantum Networks (QTN) can effectively handle the complexity and diversity of protein sequences.
• Score: 3.5300092061072523
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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