Rapid viral evolution and mutation pose continuous challenges to genomic surveillance and public health preparedness, while most existing computational approaches focus on analysing mutations only after they appear in real-world samples, limiting their ability to support early intervention. This paper presents the Quantum Bio-Mutation Foresight Simulator (Q-BMFS), a hybrid quantum–classical framework designed to explore and evaluate potential future mutation pathways before they naturally emerge. The proposed system combines quantum generative models with classical machine learning to simulate a large bio-mutation search space and assign probabilistic and risk-based interpretations to generated variants. Quantum Generative Adversarial Networks are employed to generate candidate mutation sequences, Quantum Circuit Born Machines are used to model mutation likelihood distributions, and Quantum Reservoir Computing captures temporal evolutionary behaviour, while a transformer-based classical model interprets long-range protein sequence dependencies. Experiments conducted using publicly available viral genome datasets and synthetically generated mutation samples demonstrate that Q-BMFS achieves improved predictive accuracy and more stable mutation likelihood modelling compared with classical baselines, and structural validation using RMSD analysis further indicates that the generated mutations preserve biologically plausible folding behaviour. These results suggest that quantum-enhanced foresight modelling can support proactive mutation analysis and may serve as a useful tool for future research in predictive virology and computational biology.
Rapid viral evolution and mutation pose continuous challenges to genomic surveillance and public health preparedness, while most existing computational approaches focus on analysing mutations only after they appear in real-world samples, limiting their ability to support early intervention. This paper presents the Quantum Bio-Mutation Foresight Simulator (Q-BMFS), a hybrid quantum–classical framework designed to explore and evaluate potential future mutation pathways before they naturally emerge. The proposed system combines quantum generative models with classical machine learning to simulate a large bio-mutation search space and assign probabilistic and risk-based interpretations to generated variants. Quantum Generative Adversarial Networks are employed to generate candidate mutation sequences, Quantum Circuit Born Machines are used to model mutation likelihood distributions, and Quantum Reservoir Computing captures temporal evolutionary behaviour, while a transformer-based classical model interprets long-range protein sequence dependencies. Experiments conducted using publicly available viral genome datasets and synthetically generated mutation samples demonstrate that Q-BMFS achieves improved predictive accuracy and more stable mutation likelihood modelling compared with classical baselines, and structural validation using RMSD analysis further indicates that the generated mutations preserve biologically plausible folding behaviour. These results suggest that quantum-enhanced foresight modelling can support proactive mutation analysis and may serve as a useful tool for future research in predictive virology and computational biology.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning’s role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug-receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual's genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions. This paper provides a comprehensive analysis of the potential, challenges, and future directions of quantum-AI synergy in transforming modern healthcare.
Quantum machine learning (QML) is a cross-disciplinary subject made up of two of the most exciting research areas: quantum computing and classical machine learning (ML), with ML and artificial intelligence (AI) being projected as the first fields that will be impacted by the rise of quantum machines. Quantum computers are being used today in drug discovery, material & molecular modelling and finance. In this work, we explore emerging research areas in the application of hybrid classical quantum machine learning (QML) in finance. We focus on hybrid classical-quantum models that have gained significant interest in the financial world for various applications. We utilise a real-world cryptocurrency dataset to train a quantum generative adversarial networks (qGAN), and implement the real-life NVIDIA stock price data on quantum LSTM (QLSTM), comparing its performance with classical Long Short Term Memory (LSTM) model. Additionally, we investigate the application of quantum circuit Born machine (QCBM) using a toy dataset for applications in finance. For the qGAN, we define quantum circuits for generators and showcase the potential for future quantum advantage in finance through QML, possibly in combination with QLSTM and QCBM. Our findings suggest that, while training classical data is more efficient on LSTM, the test predictions of QLSTM is slightly superior to its classical counterpart making it a promising candidate for financial forecasting and analysis for timeseries type data.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI\\\'s predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning's role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug- receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual\\\'s genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions.
Quantum computing has reintroduced itself as a breakthrough technology for machine learning, especially in the analysis of massive datasets, especially the genomic ones. The present research focuses on using QML, namely QSVM, to parse sequences, discover features relating to multiple genetic alterations, biomarkers, and gene expression patterns. The data type under consideration, namely genomic data, is high-dimensional and complex, which presents some problems to the computational methods originating from traditional mathematics. QSVM is designed to work with the mathematical foundations of quantum mechanics including superposition and entanglement and for this reason, it solves problems associated with large scale genomic data more efficiently and accurately. The application of QSVM in MEPs confirms its superiority over classical counterparts in classifying gene mutations, clustering of expression profiles or prediction of disease susceptibility. In addition, through feature mapping, QSVM offers better insights into biological information contained on genomic data, as compared to linear correlations. Furthermore, the ability of QML frameworks to expand to fit the increasing amount of data produced by next-generation sequencing technologies is assessed. Leveraging on this research QSVM has been recognised to redefine precision medicine by rapidly diagnosing diseases and providing unique therapeutic options. The results re-echo the potential of QML in transforming CG and promoting future innovations in biomedicine and health.
A key component of the comprehending genetic variants and their biological effects is the rapid identification of protein mutations. For mutation classification, a novel machine learning framework is used in this study that combines quantum and classical deep learning techniques. Protein sequence data from the National Centre for Biotechnology Information (NCBI) is used and undergo preprocessing, artificial mutation simulation, and one-hot encoding to convert sequences into numerical representations. Feature selection is performed using Ant Colony Optimization (ACO) to reduce dimensionality and enhance computational efficiency. The classification process uses a Quantum Long Short-Term Memory (QLSTM) network with linear, variational quantum circuit (VQC), fully connected, dropout, and output layers. The hybrid model successfully separates mutant and non-mutated protein sequences and the hybrid methodology's overall accuracy was 95%, according to experimental assessment, providing a viable approach for bioinformatics applications that make use of quantum computing.
This review explores the integration of quantum machine learning (QML) algorithms into real-time epidemic surveillance systems and health policy simulation frameworks. As conventional machine learning models face scalability and interpretability challenges with high-dimensional epidemiological data, QML offers a transformative paradigm by leveraging quantum computing principles to enhance pattern recognition, data compression, and forecasting precision. The study examines emerging QML architectures and their application to real-time tracking of infectious diseases, anomaly detection in public health datasets, and dynamic modeling of transmission patterns. Additionally, it assesses the role of QML in simulating the impact of health interventions, policy shifts, and behavioral dynamics on epidemic trajectories. Emphasis is placed on multi-modal data integration from genomics, environmental monitoring, and mobility trends to refine predictive accuracy. Key implementation challenges—such as quantum decoherence, algorithmic instability, regulatory constraints, and limited access to quantum hardware—are critically analyzed. The paper concludes by proposing a roadmap for the adoption of hybrid quantum-classical frameworks and policy-informed computational modeling to strengthen global epidemic preparedness and responsive health governance.