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Hiv-1 Protease Cleavage Sites Detection With A Quantum Convolutional Neural Network Algorithm.

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Hiv-1 Protease Cleavage Sites Detection With A Quantum Convolutional Neural Network Algorithm.

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Related Experiment Video

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

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 Choi^1,2, Junho Lee^3,4, Kyle L Jung^3,4

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

Scientific Reports

|June 11, 2026

View abstract on PubMed

Summary

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.

Keywords:

Cleavage sites classification HIV-1 protease cleavage Quantum convolutional neural network Quantum machine learning

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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.
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.