International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)
Harnessing Quantum Computing for Precision Medicine: Quantum Approximate Optimization Algorithm (QAOA) for Drug Discovery, Genomic Analysis, and Healthcare Optimization
M. P. Karthikeyan, S.J. Sadheesh Kumar, Manimuthu.P, S.Sridevi
Quantum computing has emerged as a promising paradigm for solving computationally intractable problems in healthcare, particularly within the scope of precision medicine. Traditional approaches to drug discovery, genomic analysis, and healthcare optimisation face significant limitations due to the exponential complexity of biological and clinical datasets. This article discusses the use of the Quantum Approximate Optimisation Algorithm (QAOA) as a quantum-classical hybrid framework to solve these issues. In drug discovery, QAOA provided better binding affinity prediction and lower root mean square deviation (RMSD) than traditional docking techniques. In genomic analysis, QAOA-boosted feature selection enhanced classification accuracy at the cost of reduced convergence time. Likewise, QAOA-based scheduling yielded better performance than traditional heuristics in healthcare optimisation by providing greater resource usage with fewer iterations. Experimental results show that warm-starting QAOA with classical initialisation reduces convergence further by up to 30%. These results highlight the promise of QAOA in speeding up biomedical computations and pave the way for scalable, quantum-enabled solutions in precision medicine.
Quantum computing has emerged as a promising paradigm for solving computationally intractable problems in healthcare, particularly within the scope of precision medicine. Traditional approaches to drug discovery, genomic analysis, and healthcare optimisation face significant limitations due to the exponential complexity of biological and clinical datasets. This article discusses the use of the Quantum Approximate Optimisation Algorithm (QAOA) as a quantum-classical hybrid framework to solve these issues. In drug discovery, QAOA provided better binding affinity prediction and lower root mean square deviation (RMSD) than traditional docking techniques. In genomic analysis, QAOA-boosted feature selection enhanced classification accuracy at the cost of reduced convergence time. Likewise, QAOA-based scheduling yielded better performance than traditional heuristics in healthcare optimisation by providing greater resource usage with fewer iterations. Experimental results show that warm-starting QAOA with classical initialisation reduces convergence further by up to 30%. These results highlight the promise of QAOA in speeding up biomedical computations and pave the way for scalable, quantum-enabled solutions in precision medicine.
With the growing complexity of biomedical data, molecular interactions and genomic architectures, there are significant challenges in adapting traditional healthcare analytics and drug discovery systems to meet computational demands. The paradigm of quantum computing has recently come into the spotlight as a potential method to solve multidimensional optimization, molecular simulation and genomic inference problems using concepts like superposition, entanglement, and quantum parallelism. This chapter examines quantum algorithms and hybrid quantum-classical architectures in the drug discovery, protein folding, genomic analysis, and precision medicine applications. It also explores quantum machine learning algorithms such as VQE, QAOA, QSVM, and QNN, which are used for intelligent biomedical analytics. In addition, the conversation also covers cloud-edge quantum healthcare infrastructures, post-quantum security, and real-world healthcare applications, further demonstrating the potential impact of quantum-enhanced healthcare systems.
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 computing has emerged as a disruptive paradigm which can transform the information heavy areas of knowledge such as precision medicine. The expanding needs of precision medicine based on customized therapy in the light of genomics, molecular profiling, and clinical data suggest that the computation tasks can demand computational capabilities unattainable by the traditional high-performance computing. Quantum algorithms, which are founded upon the laws of superposition, entanglement and quantum parallelism, offer a scale factor of exponential on a problem in genomics, drug discovery, medical imaging and biomarker discovery. This is a review article that is a narrative synthesis of emerging trends at the interface of quantum computing and precision medicine. In particular, it discusses women topics such as patient stratification and imaging analysis with quantum machine learning (QML), protein-ligand interactions and drug discovery with quantum chemistry simulations, and quantum-classical architectures to combine complex biomedical data with clinical decision-making. Initial implementations show evidence of proof-of-concept utility in accelerating the sequencing of genomes, improving the accuracy of diagnostic imaging, and improving treatment design. With those developments, there are still major issues, such as the problem of qubit coherence, scalability of algorithms, barriers to the integration of data, and ethical concerns about transparency and fair access. To overcome these obstacles, it is necessary to physically co-evolve domain-specific quantum algorithms, powerful error-correction protocols, and cloud-friendly frameworks that can connect biomedical research to current quantum computing devices. This review offers a roadmap on how quantum computing can be leveraged to drive precision medicine to scalable, interpretable, and patient-centered healthcare solutions by outlining both early uses and future opportunities.
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 represents a promising frontier in healthcare, offering unparalleled computational power to tackle complex challenges in drug discovery and precision drugs. This paper explores the potential applications and use cases of quantum computing in healthcare. We discuss how quantum algorithms can revolutionize molecular modeling, accelerating the identification of new drug candidates and predicting their interactions with biological targets with unprecedented accuracy. Likewise, we explore how quantum computing can optimize treatment plans in precision medicine by analyzing vast datasets to tailor therapies to individual patient's genetic makeup and medical histories. Additionally, we examine the challenges and opportunities associated with integrating quantum computing into healthcare systems, including considerations around scalability, algorithm development, and data security. By harnessing the transformative capabilities of quantum computing, we envision a future where healthcare delivery is enhanced, personalized, and optimized to improve patient outcomes and advance medical science.
Due to its capacity to handle information in fundamentally new ways, leading to computational powers that were previously unreachable, the multidisciplinary subject of quantum computing has recently grown and attracted significant interest from both academia and industry. Quantum computing has great promise, but how exactly it will change healthcare is still largely unknown. The potential of quantum computing to transform compute-intensive healthcare tasks like drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization is the primary focus of this survey paper, which offers the first comprehensive analysis of quantum computing's diverse capabilities in improving healthcare systems. A new era in healthcare is on the horizon, thanks to quantum computing and AI coming together to transform complicated biological simulations, the processing of genetic data, and advances in drug development. Biological data may be extremely large and complicated, making it difficult for traditional computing tools to handle. This slows down and impairs the accuracy of medical discoveries. Combining the predictive power of AI with the exponential processing speed of quantum computers presents a game-changing opportunity to speed up biological research and clinical applications. The function of quantum machine learning in improving drug discovery molecular dynamics simulations powered by artificial intelligence is discussed in this article. Quickly modeling chemical interactions, analyzing drug-receptor binding affinities, and predicting pharmacokinetics with extraordinary precision are all possible with quantum-enhanced algorithms. To further improve disease progression prediction and therapeutic target identification, we also investigate quantum-assisted deep learning models for understanding complex biological processes like protein folding, epigenetic changes, and connections between metabolic pathways.