Recently, there has been significant interest in quantum technology, which utilizes various quantum phenomena, including quantum coherence, entanglement, interference, and superposition, among others. Consequently, many attempts are recently being made to apply quantum computing in biological sciences due to its potential advancements. This paper seeks to fill that gap by highlighting the direct biological applications of quantum computing, providing a novel contribution to the field. The Goal of this paper is to offer a thorough examination of both current and potential applications of this technology, within the field of biology. First, quantum computing principles and the challenges they face are explained. Quantum machine learning, is also, introduced which opens a window to biological applications of quantum computation. Then, numerous, existing, potentially crucial, biological applications of quantum computing, which greatly enhance healthcare and quality of life, particularly in the broad field of genetics, drug design, molecular biology, and bioinformatics, are being discussed.
Recently, there has been significant interest in quantum technology, which utilizes various quantum phenomena, including quantum coherence, entanglement, interference, and superposition, among others. Consequently, many attempts are recently being made to apply quantum computing in biological sciences due to its potential advancements. This paper seeks to fill that gap by highlighting the direct biological applications of quantum computing, providing a novel contribution to the field. The Goal of this paper is to offer a thorough examination of both current and potential applications of this technology, within the field of biology. First, quantum computing principles and the challenges they face are explained. Quantum machine learning, is also, introduced which opens a window to biological applications of quantum computation. Then, numerous, existing, potentially crucial, biological applications of quantum computing, which greatly enhance healthcare and quality of life, particularly in the broad field of genetics, drug design, molecular biology, and bioinformatics, are being discussed.
Quantum life science is a rapidly developing interdisciplinary domain that combines concepts from quantum physics with biological sciences to investigate the underlying mechanisms of living systems at the molecular and cellular levels. The principles of quantum mechanics, including quantum tunneling, superposition, and entanglement, are believed to contribute to various biological processes and may help explain phenomena that cannot be fully understood through classical biological models. Studying these quantum effects in living organisms remains difficult because biological environments are highly dynamic and are affected by factors such as decoherence and energy dissipation, which can mask quantum behavior. To overcome these limitations, researchers are employing sophisticated tools and methodologies such as biological nano-scale quantum sensors, quantum-enhanced hyperpolarized MRI and NMR techniques, ultrafast two-dimensional electronic spectroscopy, and advanced computational modeling. The integration of these technologies is accelerating progress in quantum biology and related disciplines. As the field continues to evolve, it offers promising opportunities for advancing knowledge in genetics, molecular biology, medicine, biotechnology, and bioengineering, potentially leading to innovative diagnostic and therapeutic applications.
Abstract Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
The use of quantum computing1 in healthcare represents a significant shift with the potential to revolutionize medical research, diagnostics, and treatment. Quantum computing can process and analyze large datasets much faster than classical computing, offering new opportunities in healthcare. This paper examines the various ways quantum computing can be applied in healthcare, such as in drug discovery, genetic research, personalized medicine, and complex disease modeling. By using quantum algorithms, researchers can identify new treatment targets and simulate the effects of drugs on human biology in ways that were previously impossible, which can reduce the time and cost of bringing new drugs to market. Quantum computing also allows for analyzing large genomic datasets2, which can help tailor treatments to individual genetic profiles. Additionally, this technology helps in modeling complex biological systems and disease processes, providing insights into the mechanisms of diseases at a molecular level and enabling the development of more effective treatments. However, integrating quantum computing into healthcare comes with challenges such as technical limitations, the need for specialized expertise, data security concerns, and ethical considerations. This paper provides a comprehensive overview of the potential applications of quantum computing in healthcare, addresses the associated challenges, and discusses the future directions of this growing field, emphasizing the importance of interdisciplinary collaboration in fully utilizing quantum technologies in medicine. In recent years, quantum computing has rapidly advanced and attracted significant interest from academia and industry due to its unique ability to process information differently, resulting in previously unattainable computational capabilities. Despite its potential, the full impact of quantum computing on healthcare has not been thoroughly explored. This survey paper provides a systematic analysis of quantum computing's various capabilities in improving healthcare systems, focusing on its potential to transform compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing4, medical imaging5, and operational optimization. Through a comprehensive review of existing literature, we have developed taxonomies6 covering different aspects, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, offering a comprehensive view of the quantum computing paradigm for healthcare. Our survey aims to assist new and experienced researchers in quantum computing and healthcare by helping them comprehend the current research landscape, identify potential opportunities and challenges, and make informed decisions when developing new architectures and applications for quantum computing in healthcare
The convergence of quantum computing (QC) and biopharmaceutical science promises to revolutionize molecular design and predictive pharmacology. Quantum computing — harnessing principles of superposition, entanglement and quantum algorithms — enables simulation of molecular interactions with a precision and scale beyond the reach of classical methods. This review outlines the fundamental principles of QC, discusses current applications in drug discovery and pharmacology, highlights hybrid quantum-classical approaches and quantum-machine learning (QML), and examines the challenges and future directions. The review suggests that, though currently at a nascent stage, QC could dramatically reduce drug development timelines, improve success rates, and pave the way for personalized medicine and complex biologics design.
Background: Quantum biology is an emerging interdisciplinary field that explores how principles of quantum mechanics-such as superposition, entanglement, tunneling, and collapse-may influence biological phenomena.Initially speculative, it now offers concrete explanations for complex life processes, prompting a re-evaluation of biology at the quantum scale.Method: This is a narrative review based on an analysis of peer-reviewed literature from indexed journals.Key biological processes were examined in the context of quantum principles.Applications in virology, microbiology, neuroscience, and pharmacology were explored.Discussion: The integration of quantum mechanics into drug discovery and personalized medicine is reshaping therapeutic approaches.Quantum computing promises accelerated simulations of molecular interactions and enzyme dynamics.Quantum dot-based therapies and diagnostics are enhancing precision medicine.The role of superradiance and quantum coherence in neuroprotection, particularly in Alzheimer's disease, is gaining research traction.Techniques such as photodynamic therapy (PDT) and repetitive transcranial magnetic stimulation (rTMS) are being reconsidered through the lens of quantum biology.Conclusion: Quantum biology bridges quantum mechanics and life sciences, offering novel explanations for long-standing biological mysteries.Despite significant experimental challenges-especially in warm, complex biological environments-the field is advancing through ultrafast spectroscopy, cryo-electron microscopy, and quantum simulations.Continued interdisciplinary collaboration and technological innovation are essential to validate these quantum-biological interactions.Ultimately, quantum biology holds the potential to revolutionize our understanding of life, leading to groundbreaking developments in neuroscience, pharmacology, and precision medicine.
Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.