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

• URL: http://arxiv.org/abs/2510.01993v1
• Date: Thu, 02 Oct 2025 13:13:54 GMT
• Title: HIV-1 protease cleavage sites detection with a Quantum convolutional neural network algorithm
• Abstract summary: We propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins.<n>We compare the classification performance against classical neural networks under both noiseless and noisy simulations.<n>This study presents the first application of NQE-augmented QCNNs for HIV-1 cleavage site classification, providing new insights into scalable and noise-resilient quantum machine learning for biomedical data.
• Score: 10.118236414441105
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this study, we propose a quantum convolutional neural network (QCNN)-based framework with the neural quantum embedding (NQE) to predict HIV-1 protease cleavage sites in amino acid sequences from viral and human proteins. To assess the effectiveness and robustness of our framework, we compared the classification performance against classical neural networks under both noiseless and noisy simulations. Among experimental conditions, the QCNN with the angle and amplitude encoding NQE conditions consistently outperformed classical counterparts in both the similar trainable parameter scale and the different number of qubits (the averaged performance of the 4-qubits and 8-qubits QCNN: 0.9146 and 0.8929 / the averaged performance of the classical neural network: 0.6125 and 0.8278). The QCNN with the NQE showed stable performance under the quantum hardware noise, confirming its applicability to biomedical data analysis with the noise intermediate-scale quantum (NISQ) hardware. This study presents the first application of NQE-augmented QCNNs for HIV-1 cleavage site classification, providing new insights into scalable and noise-resilient quantum machine learning for biomedical data.

Related papers

• Quantum-Enhanced Neural Contextual Bandit Algorithms [50.880384999888044]
  This paper introduces the Quantum Neural Tangent Kernel-Upper Confidence Bound (QNTK-UCB) algorithm.<n>QNTK-UCB is a novel algorithm that leverages the Quantum Neural Tangent Kernel (QNTK) to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T09:58:14Z)
• HQCNN: A Hybrid Quantum-Classical Neural Network for Medical Image Classification [0.0]
  We propose a novel Hybrid Quantum-Classical Neural Network (HQCNN) for both binary and multi-class classification.<n>The architecture of HQCNN integrates a five-layer classical convolutional backbone with a 4-qubit variational quantum circuit.<n>We evaluate the model on six MedMNIST v2 benchmark datasets.
  arXiv  Detail & Related papers  (2025-09-16T08:02:53Z)
• Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection [29.259008600842517]
  This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs)<n>We implement a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator.<n>QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision.
  arXiv  Detail & Related papers  (2025-01-28T16:07:12Z)
• Quantum Neural Networks: A Comparative Analysis and Noise Robustness Evaluation [4.2435928520499635]
  In current noisy intermediate-scale quantum (NISQ) devices, hybrid quantum neural networks (HQNNs) offer a promising solution.<n>We conduct an extensive comparative analysis of various HQNN algorithms, namely Quantum Convolution Neural Network (QCNN), Quanal Neural Network (QuanNN), and Quantum Transfer Learning (QTL)<n>We evaluate the performance of each algorithm across quantum circuits with different entangling structures, variations in layer count, and optimal placement in the architecture.
  arXiv  Detail & Related papers  (2025-01-24T11:23:26Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification [83.20479832949069]
  Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood. Here we investigate the problem-dependent power of QCs on multi-class classification tasks. Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
  arXiv  Detail & Related papers  (2022-12-29T10:46:40Z)
• Random Quantum Neural Networks (RQNN) for Noisy Image Recognition [0.9205287316703888]
  We introduce a novel class of supervised Random Quantum Neural Networks (RQNNs) with a robust training strategy. The proposed RQNN employs hybrid classical-quantum algorithms with superposition state and amplitude encoding features. Experiments on the MNIST, FashionMNIST, and KMNIST datasets demonstrate that the proposed RQNN model achieves an average classification accuracy of $94.9%$.
  arXiv  Detail & Related papers  (2022-03-03T15:15:29Z)
• On Circuit-based Hybrid Quantum Neural Networks for Remote Sensing Imagery Classification [88.31717434938338]
  The hybrid QCNNs enrich the classical architecture of CNNs by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the Land Use and Land Cover (LULC) classification, chosen as an Earth Observation (EO) use case. The results of the multiclass classification prove the effectiveness of the presented approach, by demonstrating that the QCNN performances are higher than the classical counterparts.
  arXiv  Detail & Related papers  (2021-09-20T12:41:50Z)
• Quantum convolutional neural network for classical data classification [0.8057006406834467]
  We benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classical data classification. We propose a quantum neural network model inspired by CNN that only uses two-qubit interactions throughout the entire algorithm.
  arXiv  Detail & Related papers  (2021-08-02T06:48:34Z)
• The dilemma of quantum neural networks [63.82713636522488]
  We show that quantum neural networks (QNNs) fail to provide any benefit over classical learning models. QNNs suffer from the severely limited effective model capacity, which incurs poor generalization on real-world datasets. These results force us to rethink the role of current QNNs and to design novel protocols for solving real-world problems with quantum advantages.
  arXiv  Detail & Related papers  (2021-06-09T10:41:47Z)
• Toward Trainability of Quantum Neural Networks [87.04438831673063]
  Quantum Neural Networks (QNNs) have been proposed as generalizations of classical neural networks to achieve the quantum speed-up. Serious bottlenecks exist for training QNNs due to the vanishing with gradient rate exponential to the input qubit number. We show that QNNs with tree tensor and step controlled structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures.
  arXiv  Detail & Related papers  (2020-11-12T08:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.