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 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.
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
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.
Early-stage decisions in the pharmaceutical development process carry outsized consequences for eventual clinical success, yet the computational tools underpinning these decisions remain fundamentally constrained. Computer-aided drug design (CADD) has transformed how researchers navigate chemical space and predict ligand–target interactions, but classical implementations rely on mechanical force-field approximations that fail to capture polarisation, charge transfer, and electron correlation effects central to molecular recognition and reactivity. Quantum-mechanical treatments that correctly describe these phenomena scale exponentially with system size on classical hardware, rendering them impractical for drug-relevant biomolecules. Quantum computing offers a physically motivated path beyond this scaling barrier: by exploiting superposition, entanglement, and interference, quantum algorithms can in principle simulate electronic structure with polynomial resource requirements for targeted problem classes. This article provides a theoretical review of how quantum computation integrates throughout the drug discovery pipeline, from target identification to lead optimisation. Its primary contribution is a pipeline-level mapping of quantum methods—including the Variational Quantum Eigensolver (VQE), quantum machine learning, and quantum-enhanced optimisation—to specific drug development stages. The review critically distinguishes near-term NISQ (Noisy Intermediate-Scale Quantum: current devices with 50–1,000 noisy qubits operating without full error correction) capabilities from fault-tolerant quantum computing (FTQC) requirements, quantifies current resource gaps through a worked CYP3A4 case study, and identifies algorithmic limitations (barren plateaus, ansatz expressibility, measurement overhead), hardware scalability, and error correction overhead as the principal barriers to practical deployment.
Quantum computing has emerged as a potential catalyst for addressing computational bottlenecks that increasingly constrain biotechnologies, including drug discovery, structural biology, and precision medicine. As biological datasets grow in scale and complexity, classical high-performance computing struggles to efficiently model molecular interactions and high-dimensional biological systems. Recent advances in hybrid quantum-classical algorithms and quantum-enhanced machine learning have enabled early proof-of-concept applications on noisy intermediate-scale quantum (NISQ) devices, offering new strategies for molecular simulation, pattern recognition, and optimization. In this opinion article, we critically assess where quantum computing may deliver realistic near-term value for biomedicine, emphasize hybrid workflows as the most viable path forward, and outline how quantum technologies could complement, not replace, existing computational paradigms as the field progresses toward fault-tolerant systems.
The integration of quantum computing and artificial intelligence (AI), collectively termed Quantum AI, is revolutionizing the pharmaceutical industry by reshaping the paradigms of drug discovery and development. Traditional approaches to drug design are often limited by high costs, extensive timelines, and the computational intractability of simulating complex molecular interactions. Quantum computing offers exponential processing power, enabling the accurate modeling of quantum-level chemical phenomena that classical computers struggle to handle. Concurrently, AI contributes by enhancing data interpretation, prediction of molecular behaviors, and pattern recognition across large biological datasets. Together, Quantum AI enables faster identification of drug candidates, improved simulation of drug-target interactions, and early prediction of pharmacokinetics and toxicity. Key quantum algorithms, such as the Variational Quantum Eigensolver (VQE) and quantum-enhanced machine learning, have demonstrated potential to outperform conventional methods in quantum chemical calculations. Moreover, this synergy facilitates personalized medicine by integrating patient-specific data to optimize therapeutic outcomes. Despite current challenges related to hardware scalability, algorithm optimization, and ethical integration, the future trajectory of Quantum AI is poised to redefine pharmaceutical innovation. This review underscores the transformative capabilities of Quantum AI and its potential to usher in a new era of efficient, cost-effective, and precision-driven drug development.