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Related Experiment Video

Updated: Jun 13, 2026

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing
10:18

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

Published on: October 16, 2018

HIV-1 protease cleavage sites detection with a quantum convolutional neural network algorithm.

Junggu Choi1,2, Junho Lee3,4, Kyle L Jung3,4

  • 1Microbial Sciences in Health, Cleveland Clinic Research, Cleveland Clinic, Cleveland, OH, 44195, USA. choij14@ccf.org.

Scientific Reports
|June 11, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a quantum convolutional neural network (QCNN) with neural quantum embedding (NQE) for predicting human immunodeficiency virus type 1 (HIV-1) protease cleavage sites. The quantum model significantly outperforms classical methods, showing promise for quantum machine learning in bioinformatics.

Area of Science:

  • Quantum Computing
  • Bioinformatics
  • Computational Biology

Background:

  • Human immunodeficiency virus type 1 (HIV-1) protease is vital for viral maturation, making cleavage site prediction crucial for therapeutic development.
  • Accurate prediction of HIV-1 protease cleavage sites aids in understanding viral pathogenesis and designing inhibitors.

Purpose of the Study:

  • To develop a quantum convolutional neural network (QCNN) framework integrated with neural quantum embedding (NQE) for predicting HIV-1 protease cleavage sites.
  • To enhance feature representation in quantum space for improved sequence classification accuracy.

Main Methods:

  • A QCNN-based framework with NQE was designed to predict HIV-1 protease cleavage sites from amino acid sequences.
  • The quantum model was evaluated against classical neural networks using four HIV-1 protease cleavage site datasets under simulated noisy and noiseless quantum environments.
Keywords:
Cleavage sites classificationHIV-1 protease cleavageQuantum convolutional neural networkQuantum machine learning

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Last Updated: Jun 13, 2026

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  • Experiments explored scalability and parameter efficiency using varying numbers of qubits and trainable parameters.

    • Main Results:

    • QCNN models with NQE (angle and amplitude encoding) achieved higher classification accuracy than classical neural networks, with average accuracies of 0.9146 (4-qubit) and 0.8929 (8-qubit).
    • The QCNN with NQE (ZZ feature map and angle encoding) demonstrated stable performance under simulated quantum hardware noise.
    • The quantum approach outperformed classical counterparts, even under simulated noise conditions.

    Conclusions:

    • This study presents the first NQE-augmented QCNN for HIV-1 protease cleavage site prediction, demonstrating quantum machine learning's potential in biomedical sequence analysis.
    • NQE-enhanced QCNNs show promise for scalable, noise-resilient quantum machine learning in bioinformatics.
    • The findings lay the groundwork for future quantum-based bioinformatics analyses and therapeutic inhibitor development.