Quantum materials—systems whose behavior is governed not by semiclassical approximations but by inherently quantum-mechanical phenomena—are rapidly emerging as foundational technologies for next-generation therapeutic innovation. This chapter explores how strong electronic correlations, quantum confinement, entanglement, and non-trivial topology enable new classes of biomedical tools and computational platforms with capabilities far beyond those achievable with traditional materials. Two complementary paradigms define the role of quantum materials in therapeutics. First, quantum materials function as direct therapeutic and diagnostic agents: quantum dots, graphene-based nanosystems, and other nanoscale structures exhibiting size-dependent emission, tunable fluorescence, and engineered surface chemistries enable ultra-sensitive biosensing, multimodal bioimaging, and targeted drug delivery with high precision. Second, quantum materials form the hardware substrate of quantum computation, providing superconducting, photonic, and ion-based architectures capable of simulating biological complexity and accelerating molecular discovery from first principles. This chapter outlines the physical underpinnings of quantum materials, surveys leading classes of quantum platforms—including superconducting transmons, trapped ions, and monolithic photonic processors—and evaluates their implications for drug discovery, diagnostics, and personalized medicine. By unifying the tangible nanoscale engineering of quantum materials with the abstract computational power of quantum processors, this chapter establishes a holistic framework for the future of quantum-enhanced therapeutics and highlights the roadmap toward scalable, fault-tolerant quantum platforms capable of transforming the biomedical sciences.
Quantum materials—systems whose behavior is governed not by semiclassical approximations but by inherently quantum-mechanical phenomena—are rapidly emerging as foundational technologies for next-generation therapeutic innovation. This chapter explores how strong electronic correlations, quantum confinement, entanglement, and non-trivial topology enable new classes of biomedical tools and computational platforms with capabilities far beyond those achievable with traditional materials. Two complementary paradigms define the role of quantum materials in therapeutics. First, quantum materials function as direct therapeutic and diagnostic agents: quantum dots, graphene-based nanosystems, and other nanoscale structures exhibiting size-dependent emission, tunable fluorescence, and engineered surface chemistries enable ultra-sensitive biosensing, multimodal bioimaging, and targeted drug delivery with high precision. Second, quantum materials form the hardware substrate of quantum computation, providing superconducting, photonic, and ion-based architectures capable of simulating biological complexity and accelerating molecular discovery from first principles. This chapter outlines the physical underpinnings of quantum materials, surveys leading classes of quantum platforms—including superconducting transmons, trapped ions, and monolithic photonic processors—and evaluates their implications for drug discovery, diagnostics, and personalized medicine. By unifying the tangible nanoscale engineering of quantum materials with the abstract computational power of quantum processors, this chapter establishes a holistic framework for the future of quantum-enhanced therapeutics and highlights the roadmap toward scalable, fault-tolerant quantum platforms capable of transforming the biomedical sciences.
Quantum technologies-quantum computing, quantum sensing, and quantum-enabled materials-are increasingly proposed as tools to accelerate drug discovery. Yet "quantum advantage" is frequently asserted without standardized benchmarks, clinically meaningful endpoints, or controlled comparisons against modern classical workflows. This review separates (i) quantum computing for molecular simulation and optimization, (ii) quantum sensing for structural/biophysical characterization and diagnostics, and (iii) quantum nanotechnologies for imaging and sensing, and then extends the framework to include device-led and physical therapies that increasingly co-evolve with drug development: photobiomodulation (red/NIR), focused ultrasound for blood-brain barrier opening and delivery enhancement, noninvasive neuromodulation devices (tDCS/TMS), and optogenetic therapies. We summarize demonstrated capabilities and constraints of NISQ-era computing, outline algorithmic classes for quantum chemistry and hybrid variational methods, evaluate quantum error-mitigation strategies and their limits, and contrast claimed performance with classical baselines in computational chemistry and machine learning. We conclude that near-term translational value is most substantial for quantum sensing and for device/physical platforms with established clinical evidence. In contrast, quantum computing remains principally hypothesis-generating until fault tolerance and reproducible advantage are established. Device-based modalities-including transcranial photobiomodulation for neuropsychiatric indications, focused ultrasound enabling CNS drug delivery, and home-supervised neuromodulation-are already reshaping therapeutic landscapes and clinical trial design. For drug discovery, the central requirement is not quantum novelty but validated decision impact, demonstrated under controlled benchmarks aligned with reproducibility expectations comparable to those evolving for AI/ML-driven methods in regulated contexts.
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.
Precision medicine aims to tailor healthcare by integrating individual genetic, epigenetic, transcriptomic, proteomic, and clinical data, collectively referred to as multi-omic data. However, the scale and complexity of such multi-omics datasets challenge classical computing approaches. Quantum computing, which leverages superposition and entanglement (quantum-level correlations between particles), offers a fundamentally new paradigm for accelerating molecular simulations, biomarker discovery, and high-dimensional data analysis. This review explores the convergence of quantum computing and it's potential to provide unmet needs in precision biomedicine research, with emphasis on applications in diagnostic modeling, multi-omic data integration and drug discovery. We highlight early proof-of-concept studies demonstrating the use of quantum machine learning for disease prediction, quantum algorithms for protein folding, and quantum generative models for novel drug design. Hybrid quantum-classical workflows are also already enabling gene network inference and prioritization of variants of uncertain significance, the latter of which is a major focus of multi-omic research worldwide. Emerging directions include digital twin simulations and real-time clinical decision support powered by quantum models. Looking ahead, the long-term vision for quantum computing in biomedicine involves in silico modeling of entire biological systems to simulate cellular responses to perturbations like drug treatments, enabling clinicians to test therapies in virtual patients before real-world application. Despite these advances, practical implementation remains limited by hardware constraints, qubit decoherence, algorithm scalability, and regulatory barriers. Nonetheless, as quantum hardware evolves and AI-aligned quantum algorithms mature, their integration holds transformative potential. Quantum computing may eventually shorten diagnostic timelines, improve therapeutic precision, and make biomedical innovation more globally accessible. We outline a roadmap for translating these technologies into next-generation precision medicine.
Scientific progress is evolving at an unprecedented pace, led by quantum computers. The Second Quantum Revolution harnesses superposition and entanglement for computation and sensing. Majorana fermions now enable fault‑tolerant topological qubits, while 2D monolayers like graphene revolutionize flexible electronics. Nanotechnology delivers targeted cancer therapies and quantum dots for ultra‑sensitive biomedical imaging. Beyond IT, quantum simulations accelerate drug discovery and materials design. From energy storage to environmental monitoring, these converging advances—quantum, nano, and materials science—are driving a technological explosion unmatched in human history.
Quantum computing provides the transformative capability to take advancements in AI in medicine several notches ahead. Building on the principles of quantum mechanics, quantum computing can supply computational speed and efficiency that leave the limitations of classical computers far behind, especially as they deal with complex data sets in medicine. As such, this chapter provides a look at the frontier areas of intersection between quantum computing and AI in medicine, particularly in drug discovery, genomics, precision medicine, and medical imaging. The accelerated simulation of molecular interactions would promise quantum algorithms to find therapeutic targets quickly and to optimize treatment pathways. Quantum machine learning also promises breakthroughs in diagnostic accuracy and predictive analytics, which will provide personalized care. This chapter explores the potential of quantum computing in medical advancement, focusing on hardware scalability, algorithm development, data privacy, and ethical considerations.
Sarfaraz K Niazi Department of Pharmaceutical Sciences, University of Illinois, Chicago, IL, 60612, USACorrespondence: Sarfaraz K Niazi, Email sniazi3@uic.eduAbstract: Quantum technologies—quantum computing, quantum sensing, and quantum-enabled materials—are increasingly proposed as tools to accelerate drug discovery. Yet “quantum advantage” is frequently asserted without standardized benchmarks, clinically meaningful endpoints, or controlled comparisons against modern classical workflows. This review separates (i) quantum computing for molecular simulation and optimization, (ii) quantum sensing for structural/biophysical characterization and diagnostics, and (iii) quantum nanotechnologies for imaging and sensing, and then extends the framework to include device-led and physical therapies that increasingly co-evolve with drug development: photobiomodulation (red/NIR), focused ultrasound for blood-brain barrier opening and delivery enhancement, noninvasive neuromodulation devices (tDCS/TMS), and optogenetic therapies. We summarize demonstrated capabilities and constraints of NISQ-era computing, outline algorithmic classes for quantum chemistry and hybrid variational methods, evaluate quantum error-mitigation strategies and their limits, and contrast claimed performance with classical baselines in computational chemistry and machine learning. We conclude that near-term translational value is most substantial for quantum sensing and for device/physical platforms with established clinical evidence. In contrast, quantum computing remains principally hypothesis-generating until fault tolerance and reproducible advantage are established. Device-based modalities—including transcranial photobiomodulation for neuropsychiatric indications, focused ultrasound enabling CNS drug delivery, and home-supervised neuromodulation—are already reshaping therapeutic landscapes and clinical trial design. For drug discovery, the central requirement is not quantum novelty but validated decision impact, demonstrated under controlled benchmarks aligned with reproducibility expectations comparable to those evolving for AI/ML-driven methods in regulated contexts.Keywords: quantum computing, quantum sensing, drug discovery, quantum chemistry, error mitigation, photobiomodulation, focused ultrasound, neuromodulation, optogenetics, regulatory science