DNA methylation is a critical epigenetic modification that regulates gene expression and genome stability, primarily catalyzed by the DNA methyltransferase 3 (DNMT3) family of enzymes. Distinguishing precise sequence-specific flanking preferences between DNMT3A and DNMT3B remains computationally challenging due to the complex and high-dimensional nature of biochemical sequence data. In this study, we propose a novel Quantum Machine Learning (QML) approach to classify DNA methylation preferences, leveraging a Variational Quantum Classifier (VQC). Using experimentally derived deep enzymology data, we constructed a dataset containing 4,096 unique NNNCGNNN motifs. We evaluated a four-qubit VQC using a custom NumPy exact-statevector simulator. The described two-layer circuit contains 24 trainable rotations, eight CNOT gates, and eight input-encoding rotations, totaling 40 gates before measurement. Across five initialization seeds, the revised VQC evaluation achieved a ROC-AUC of 0.661 pm 0.009, where variability denotes sample standard deviation. The held-out test partition is approximately balanced, containing 200 DNMT3A-labeled motifs and 210 DNMT3B-labeled motifs. An always-DNMT3B predictor achieves 51.2% accuracy and a balanced accuracy of 0.500 on this test partition. This proof-of-concept establishes a foundation for utilizing near-term quantum architectures in complex epigenetic classification tasks, paving the way for hybrid quantum-classical pipelines in bioinformatics.
DNA methylation is a critical epigenetic modification that regulates gene expression and genome stability, primarily catalyzed by the DNA methyltransferase 3 (DNMT3) family of enzymes. Distinguishing precise sequence-specific flanking preferences between DNMT3A and DNMT3B remains computationally challenging due to the complex and high-dimensional nature of biochemical sequence data. In this study, we propose a novel Quantum Machine Learning (QML) approach to classify DNA methylation preferences, leveraging a Variational Quantum Classifier (VQC). Using experimentally derived deep enzymology data, we constructed a dataset containing 4,096 unique NNNCGNNN motifs. We evaluated a four-qubit VQC using a custom NumPy exact-statevector simulator. The described two-layer circuit contains 24 trainable rotations, eight CNOT gates, and eight input-encoding rotations, totaling 40 gates before measurement. Across five initialization seeds, the revised VQC evaluation achieved a ROC-AUC of 0.661 pm 0.009, where variability denotes sample standard deviation. The held-out test partition is approximately balanced, containing 200 DNMT3A-labeled motifs and 210 DNMT3B-labeled motifs. An always-DNMT3B predictor achieves 51.2% accuracy and a balanced accuracy of 0.500 on this test partition. This proof-of-concept establishes a foundation for utilizing near-term quantum architectures in complex epigenetic classification tasks, paving the way for hybrid quantum-classical pipelines in bioinformatics.
Abstract Hybrid quantum–classical architectures offer a promising direction for sequence modeling, yet their empirical behavior on realistic bioinformatics tasks remains insufficiently documented. Here, we present a fully simulated, reproducible benchmark for hybrid quantum machine learning applied to synthetic DNA disease-risk classification. A dataset of 5,000 sequences (200 bp) across five balanced classes was generated using motif-injection rules with controlled noise, from which 74 biological features were extracted. Baseline models (CNN, attention, classical ensemble) were compared against two quantum-hybrid models incorporating a 4-qubit ZZFeatureMap + RealAmplitudes ansatz, executed exclusively on Qiskit Aer Simulator with 1,024 shots. The attention model achieved the highest test accuracy (51.7%), while quantum-hybrid models produced comparable performance (51.1–51.3%), showing no measurable quantum advantage under these settings. The study establishes an honest, fully reproducible baseline for QML in genomics, highlighting current limitations of small-qubit encodings and motivating future work with trainable variational circuits and larger biological datasets.
Abstract Hybrid quantum–classical architectures offer a promising direction for sequence modeling, yet their empirical behavior on realistic bioinformatics tasks remains insufficiently documented. Here, we present a fully simulated, reproducible benchmark for hybrid quantum machine learning applied to synthetic DNA disease-risk classification. A dataset of 5,000 sequences (200 bp) across five balanced classes was generated using motif-injection rules with controlled noise, from which 74 biological features were extracted. Baseline models (CNN, attention, classical ensemble) were compared against two quantum-hybrid models incorporating a 4-qubit ZZFeatureMap + RealAmplitudes ansatz, executed exclusively on Qiskit Aer Simulator with 1,024 shots. The attention model achieved the highest test accuracy (51.7%), while quantum-hybrid models produced comparable performance (51.1–51.3%), showing no measurable quantum advantage under these settings. The study establishes an honest, fully reproducible baseline for QML in genomics, highlighting current limitations of small-qubit encodings and motivating future work with trainable variational circuits and larger biological datasets.
Quantum Machine Learning (QML) is a newly emerging research area at the intersection of classical machine learning (CML) and quantum computing (QC). Data is becoming voluminous rapidly, so it is challenging for classical computers to train Machine Learning (ML) models over massive datasets. The hope is that quantum physics' intrinsic features, such as entanglement, superposition, and interference, could be exploited as resources for training ML models on big datasets that would otherwise be relatively impossible for classical computers. It is theoretically proven that quantum computers have an exponential-time advantage over their classical counterparts in solving several problems, e.g., complex large dimensional matrix multiplication, factorization problem, unstructured database search, etc. QML models attempt to find a quantum advantage over their classical counterparts. Variational Quantum Classifiers (VQC) are hybrid quantum neural networks to perform the task of classification using QML models. VQC models in the present NISQ (Noisy Intermediate Scale Quantum — 50 to 100 qubits) era can produce comparable and even better results than Classical models. In this article, we examined the performance of a VQC while performing a simple binary classification task. In this article, we use a VQC to evaluate this method's performance empirically. We constructed a VQC to predict the label of fresh input for the typical Iris dataset comprising pairings of target outputs and training inputs. Our quantum classifier can reasonably predict species labels using only four qubits. These levels are fairly good compared to the accuracy levels attained by classical classifiers.
This study investigates classification performance and trainability of variational quantum classifiers on near-term noisy quantum hardware across circuit depth and qubit count regimes within the context of quantum computing and machine learning, an area of growing scientific importance given its implications for hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment. Using gradient-based parameter optimization of parameterized quantum circuits with hardware noise modeling via density matrix simulation and physical execution on IBM Eagle processors, we examine expressibility of parameterized circuits enabling nonlinear decision boundaries in feature Hilbert space, with trainability constrained by barren plateau gradients at large qubit counts in 96 circuit architectures (4 qubit counts x 4 depths x 6 ansatz families) evaluated across 5 classification datasets with 20 random initialization seeds each drawn from IBM Quantum Eagle (127-qubit) and Falcon (27-qubit) processors with noise mitigation via zero-noise extrapolation. Results indicate that 10-qubit, 4-layer VQC achieves 84.2% classification accuracy on the best benchmark, within 3.8 percentage points of classical SVM, but gradient variance decays exponentially with qubit count (variance halving per 2 added qubits), confirming barren plateau scaling (p < 0.001), with 84.2% accuracy, 3.8 pp gap to classical SVM baseline as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to quantum computing and machine learning and carry actionable implications for the design of programs and policies targeting hybrid quantum-classical algorithm design and practical NISQ hardware benchmarking for near-term quantum advantage assessment.
Quantum Machine Learning (QML) has emerged as a compelling research domain that unites quantum computing principles with classical learning theory to address computationally demanding classification problems.This paper investigates two principal QML architectures-Variational Quantum Classifiers (VQC) and Quantum Neural Networks (QNN)-within the context of binary classification.We present an end-to-end hybrid quantum-classical pipeline applied to the UCI Mushroom dataset, comprising 22 categorical attributes, and demonstrate that quantumenhanced models can achieve competitive classification performance under present-day hardware constraints.The pipeline integrates Quantum Random Access Code (QRAC) encoding for dimensionality reduction, ZZFeatureMapbased quantum feature mapping, RealAmplitudes parameterized ansatz, and classical optimizer-driven parameter updates using the COBYLA algorithm.The complete system is deployed through a Flask web interface and containerized with Docker for reproducibility.Experimental results on a quantum simulator indicate a classification accuracy of 87.4%, precision of 0.891, and recall of 0.863, establishing the practical viability of NISQ-era quantum classifiers for structured categorical data.The paper further analyzes convergence behavior, circuit depth effects, and comparative performance against classical baselines, providing insights into the strengths and limitations of current QML architectures for real-world classification deployment.
DNA microarray technology enables the simultaneous measurement of expression levels of thousands of genes, thereby facilitating the understanding of the molecular mechanisms underlying complex diseases such as brain tumors and the identification of diagnostic genetic signatures. To derive meaningful biological insights from the high-dimensional and complex gene features obtained through this technology and to analyze gene properties in detail, classical artificial intelligence (AI)-based approaches such as machine learning (ML) and deep learning (DL) are widely employed. However, these methods face various limitations in managing high-dimensional vector spaces and modeling the intricate relationships among genes. In particular, challenges such as hyperparameter tuning, computational costs, and high processing power requirements can hinder their efficiency. To overcome these limitations, quantum computing and quantum computing-based AI approaches are gaining increasing attention. Leveraging quantum properties such as superposition and entanglement, quantum methods enable more efficient parallel processing of high-dimensional data and offer faster and more effective solutions to problems that are computationally demanding for classical methods. In this study, a novel model called “Deep VQC” is proposed, based on the Variational Quantum Classifier (VQC) approach. Developed using microarray data containing 54,676 gene features, the model successfully classified four different types of brain tumors-ependymoma, glioblastoma, medulloblastoma, and pilocytic astrocytoma-alongside healthy samples with high accuracy. Furthermore, compared to classical ML algorithms, the Deep VQC model demonstrated either superior or comparable classification performance. These results highlight the potential of quantum AI methods as an effective and promising approach for the analysis and classification of complex structures such as brain tumors based on gene expression features.