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arXiv:1212.5005 · Linear Algebra and its Applications

Computations in quantum tensor networks

Thomas Huckle, Konrad Waldherr, Thomas Schulte‐Herbrüggen

Published Jan 27, 2012• 57 citations
arXivHTMLPDF
DOI
10.1016/j.laa.2011.12.019

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Decoding Protein Sequences as a 'Quantum Language'… Demonstration of a QNLP-Based Classification Pipeline

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A study published on bioRxiv demonstrates for the first time the technique of treating protein sequences like sentences in natural language processing (NLP) and parsing them into parameterized quantum circuits using the Quantum Natural Language (QNLP) framework. It establishes a scheme that converts protein sequences into tensor networks represented by wires and boxes, and maps them onto actual quantum circuits through semantic functors. By generating protein syntax trees with a neural network-based parser and converting them into string diagrams optimized for quantum processor execution, the study proves that 'Quantum Tensor Networks (QTN)' achieve overwhelming computational accuracy in classifying vast protein sequences. [Quantum Biology Society] An innovative approach has been proposed to decode protein sequences—the most fundamental and complex codes of living organisms—by converting them into a natural language that quantum computers can comprehend. The paper titled "Application of Quantum Tensor Networks for Protein Classification", published in the preprint repository bioRxiv, introduces a groundbreaking pipeline that applies Quantum Natural Language Processing (QNLP) technology to protein sequence classification, opening a new horizon for next-generation bioinformatics. A research team from Pennsylvania State University, including Debarshi Kundu, Jian Wang, Archisman Ghosh, and Nikolay Dokholyan, led this innovative study. ■ QNLP: Reading Protein Sequences as Sentences The researchers introduced a unique perspective of treating protein sequences, which are arrays of amino acids, as if they were sentences composed in human language. To achieve this, they adopted the 'Quantum Natural Language (QNLP)' framework, which combines state-of-the-art computational natural language processing (NLP) techniques with quantum computing. Within this framework, protein sequences are parsed into syntax trees with grammatical structures through a neural network-based parser, laying the foundation for their direct conversion into parameterized quantum circuits. ■ Transformation into Tensor Networks and String Diagrams In this process, protein sequences are converted into a tensor network scheme, geometrically represented by wires and boxes. The researchers defined a sophisticated semantic function (functor) that perfectly maps this abstract network to the operational structure of actual quantum circuits. The generated syntax trees are ultimately transformed into string diagrams, a format optimized for execution on quantum processors. Consequently, the constructed Quantum Tensor Networks (QTN) demonstrated overwhelming computational accuracy in machine learning tasks that involve extracting features and functionally classifying highly diverse and complex protein sequences, even in environments with a limited number of available qubits. This demonstration is highly significant as it establishes a powerful workflow in drug discovery and biotechnology, enabling rapid and accurate analysis of massive genomic data and protein interaction structures through quantum algorithms, transcending the limits of conventional classical computers. #QuantumTensorNetworks #QNLP #ProteinClassification #QuantumMachineLearning #QuantumLanguageModel #ProteinSequenceAnalysis #QuantumBiology #Biophysics #ArtificialIntelligence #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2024.03.11.584501v1

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The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The architecture of QTN is composed of a parametric tensor-train network for feature extraction and a tensor product encoding for quantum embedding. We highlight the QTN for quantum embedding in terms of two perspectives: (1) we theoretically characterize QTN by analyzing its representation power of input features; (2) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.
Recently, there has been growing interest in medical image analysis methods based on multi-layer perceptron (MLP), which have demonstrated remarkable performance in this field. Typically, the MLP has been employed to flatten feature maps along the channel and spatial dimensions and encode them to capture the overall feature information. However, for MLP-based architecture, the whole learning process requires high computational complexity, a large number of parameters, and large-scale training datasets. Meanwhile, quantum tensor network (QTN), derived from quantum many-body systems, have gained popularity in machine learning due to their ability to reduce the number of parameters and explore the correlation of remote elements in an image through a tensor contraction operation. In this paper, we propose a novel framework named QTN-MLP based on QTN and MLP to tackle the weaknesses of MLP. This paper has been extensively validated on the COVID-CT dataset and ISIC 2018 dataset, and the experiments show that our method competes with state-of-the-art methods with lower computational complexity and fewer parameters.
A study published on bioRxiv demonstrates for the first time the technique of treating protein sequences like sentences in natural language processing (NLP) and parsing them into parameterized quantum circuits using the Quantum Natural Language (QNLP) framework. It establishes a scheme that converts protein sequences into tensor networks represented by wires and boxes, and maps them onto actual quantum circuits through semantic functors. By generating protein syntax trees with a neural network-based parser and converting them into string diagrams optimized for quantum processor execution, the study proves that 'Quantum Tensor Networks (QTN)' achieve overwhelming computational accuracy in classifying vast protein sequences. [Quantum Biology Society] An innovative approach has been proposed to decode protein sequences—the most fundamental and complex codes of living organisms—by converting them into a natural language that quantum computers can comprehend. The paper titled "Application of Quantum Tensor Networks for Protein Classification", published in the preprint repository bioRxiv, introduces a groundbreaking pipeline that applies Quantum Natural Language Processing (QNLP) technology to protein sequence classification, opening a new horizon for next-generation bioinformatics. A research team from Pennsylvania State University, including Debarshi Kundu, Jian Wang, Archisman Ghosh, and Nikolay Dokholyan, led this innovative study. ■ QNLP: Reading Protein Sequences as Sentences The researchers introduced a unique perspective of treating protein sequences, which are arrays of amino acids, as if they were sentences composed in human language. To achieve this, they adopted the 'Quantum Natural Language (QNLP)' framework, which combines state-of-the-art computational natural language processing (NLP) techniques with quantum computing. Within this framework, protein sequences are parsed into syntax trees with grammatical structures through a neural network-based parser, laying the foundation for their direct conversion into parameterized quantum circuits. ■ Transformation into Tensor Networks and String Diagrams In this process, protein sequences are converted into a tensor network scheme, geometrically represented by wires and boxes. The researchers defined a sophisticated semantic function (functor) that perfectly maps this abstract network to the operational structure of actual quantum circuits. The generated syntax trees are ultimately transformed into string diagrams, a format optimized for execution on quantum processors. Consequently, the constructed Quantum Tensor Networks (QTN) demonstrated overwhelming computational accuracy in machine learning tasks that involve extracting features and functionally classifying highly diverse and complex protein sequences, even in environments with a limited number of available qubits. This demonstration is highly significant as it establishes a powerful workflow in drug discovery and biotechnology, enabling rapid and accurate analysis of massive genomic data and protein interaction structures through quantum algorithms, transcending the limits of conventional classical computers. #QuantumTensorNetworks #QNLP #ProteinClassification #QuantumMachineLearning #QuantumLanguageModel #ProteinSequenceAnalysis #QuantumBiology #Biophysics #ArtificialIntelligence #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2024.03.11.584501v1