arXiv subjects

Kyle L. Jung

Publications and source records attributed to Kyle L. Jung.

3 recordsLinked to original sources

arXiv2025

Optimizing Quantum Data Embeddings for Ligand-Based Virtual Screening

Effective molecular representations are essential for ligand-based virtual screening. We investigate how quantum data embedding strategies can improve this task by developing and evaluating a family of quantum-classical hybrid embedding approaches. These approaches combine classical neural networks with parameterized quantum circuits in different ways to generate expressive molecular representations and are assessed across two benchmark datasets of different sizes: the LIT-PCBA and COVID-19 collections. Across multiple biological targets and class-imbalance settings, several quantum and hybrid embedding variants consistently outperform classical baselines, especially in limited-data regimes. These results highlight the potential of optimized quantum data embeddings as data-efficient tools for ligand-based virtual screening.

quant-ph↗

arXiv2025

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

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.

quant-ph↗

arXiv2025

Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?

Identifying key biomarkers for COVID-19 from high-dimensional multi-omics data is critical for advancing both diagnostic and pathogenesis research. In this study, we evaluated the applicability of the Quantum Support Vector Machine (QSVM) algorithm for biomarker-based classification of COVID-19. Proteomic and metabolomic biomarkers from two independent datasets were ranked by importance using ridge regression and grouped accordingly. The top- and bottom-ranked biomarker sets were then used to train and evaluate both classical SVM (CSVM) and QSVM models, serving as predictive and negative control inputs, respectively. The QSVM was implemented with multiple quantum kernels, including amplitude encoding, angle encoding, the ZZ feature map, and the projected quantum kernel. Across various experimental settings, QSVM consistently achieved classification performance that was comparable to or exceeded that of CSVM, while reflecting the importance rankings by ridge regression. Although the experiments were conducted in numerical simulation, our findings highlight the potential of QSVM as a promising approach for multi-omics data analysis in biomedical research.

quant-ph↗