Current artificial intelligence platforms remain constrained by hardware limitations, particularly parallelism and memory bandwidth, in addressing complex biomedical challenges such as protein folding, drug discovery, and large‑scale biomedical data analysis. These constraints limit the ability of the identification of optimal solutions in high-dimensional models. Quantum algorithms, by contrast, can explore large combinatorial variable spaces in parallel and converge more efficiently on optimal strategies. Although still reliant on hybrid integration with AI and classical algorithms, quantum approaches have already demonstrated the potential to alleviate key bottlenecks in biomedical research and development.
Quantum Computing Reshaping Healthcare and Enabling Future Precision Therapies
According to estimates by MarketsandMarkets, the quantum computing market in healthcare is projected to grow from USD 270 million in 2025 to USD 1.32 billion by 2030, representing a compound annual growth rate (CAGR) of 37.9%. This growth reflects the parallel computing advantages enabled by qubits. By exploiting quantum phenomena such as superposition, entanglement, and interference, quantum computing is increasingly recognized as a means to mitigate the “curse of dimensionality” in medical research.
The rapid entry of quantum technologies into healthcare is driven by rising demand for precision medicine, the limitations of conventional drug development timelines highlighted during the COVID-19 pandemic, and the emergence of Quantum-as-a-Service (QaaS) models that lower barriers to access. The integration of quantum technologies with artificial intelligence, biomedical data, and simulation platforms is expected to enhance research and development efficiency across areas such as rare‑disease therapeutics, gene and cell therapy design, mRNA modeling, and medical imaging analysis. Together, these advances signal a transition from exploratory research toward translational and application-oriented development.
Applications of Quantum Technologies in Drug Development and Clinical Research
Quantum computing is progressing from laboratory environments into pharmaceutical and clinical contexts. Leading pharmaceutical companies, including Merck, AstraZeneca, and Boehringer Ingelheim, are applying quantum simulation to lead compound design, while Moderna has adopted quantum tools to optimize nucleic‑acid‑based therapeutics. In parallel, IBM, in collaboration with the Cleveland Clinic, has deployed quantum models for clinical prediction, illustrating the expanding breadth of quantum applications across the healthcare value chain.
Scenario I: Accelerating Drug Discovery and Vaccine Development
In drug development, Merck supports SEEQC in advancing quantum hardware capabilities, while Astra Zeneca and SandboxAQ apply integrated AI–quantum platforms to molecular interaction analysis and candidate screening. Collaborative efforts involving IonQ, AstraZeneca, Amazon Web Services (AWS), and NVIDIA have reported simulation speeds up to 20 times faster than conventional approaches. Boehringer Ingelheim, working with Google Quantum AI and PsiQuantum, applies quantum simulation to Cytochrome P450 and FeMo cofactor electronic structures, significantly reducing compound screening timelines. In parallel, quantum-enabled platforms developed by start-ups such as Kuano, POLARISqb, Kvantify, and Menten AI are further strengthening early-stage drug discovery pipelines. In the area of nucleic‑acid therapeutics, IBM and Moderna utilize the Qiskit Runtime platform to accelerate mRNA design, while Creyon Bio integrates quantum chemistry with generative AI for oligonucleotide screening, attracting investment from Eli Lilly. Collectively, these developments indicate that quantum simulation is increasingly being embedded into routine pharmaceutical research and development workflows.
Scenario II: Clinical Risk Prediction and Healthcare Operations Innovation
In clinical settings, IBM and the Cleveland Clinic represent one of the earliest deployments of quantum computing within a healthcare institution, developing quantum‑enhanced predictive models for cardiovascular risk assessment. Beyond risk prediction, quantum simulation is also being applied to pharmacokinetic/ pharmacodynamic (PK/PD) and physiologically based pharmacokinetic (PBPK) modeling. In addition, quantum generative models are being explored to construct virtual placebo control groups, reducing participant requirements in rare-disease and critical‑care trials. Together, these applications highlight the potential of quantum technologies to transform clinical research processes and trial design.
Conclusion: Building a “Semiconductor × Biomedical × Quantum” Ecosystem in Taiwan
Taiwan possesses a comprehensive supply chain spanning IC design, foundry services, and system integration. When combined with ongoing academic research and development in superconducting quantum computers and quantum chip platforms, these strengths provide a solid foundation for the advancement of quantum healthcare hardware. By integrating local healthcare ecosystems with regulatory sandbox mechanisms and applying quantum technologies to high-value use cases, such as drug simulation and clinical risk prediction, Taiwan has the opportunity to cultivate an emerging “semiconductor × biomedical × quantum” innovation ecosystem.
Analysis I: Taiwan’s Clustered Supply Chain Advantage in Quantum Healthcare Development
Taiwan’s comprehensive hardware supply chain supports the accelerated deployment of quantum technologies in biomedical applications. Academia Sinica has established superconducting quantum computing systems and an eight‑inch quantum chip platform, while emerging start‑ups are developing silicon quantum dots and photonic qubits to help reduce deployment costs. In parallel, the Compal GPU Annealer, developed under the The Taiwan Quantum National Team programme by Compal Electronics as a quantum-inspired computing platform, has demonstrated potential in compound simulation, laying important groundwork for the development of quantum-inspired CDMO platforms.
Analysis II: Institutional Readiness and Pilot Integration for Quantum Healthcare Translation
Quantum healthcare is progressing from research-oriented exploration toward clinical deployment and requires systematic support from broader healthcare ecosystems. At the international level, acceleration efforts are already underway, including the deployment of quantum computing systems by IBM in collaboration with the Cleveland Clinic, as well as Australia’s strategic investment in PsiQuantum to develop photonic quantum computing technologies. While quantum computing demonstrates substantial potential in applications such as drug simulation and clinical risk prediction, its large‑scale adoption remains constrained by three major challenges:
• High hardware barriers: Quantum systems require cryogenic environments, involve high capital costs, and remain difficult to integrate into clinical settings.
• Data governance risks: Cross‑border data exchange intensifies concerns over medical data privacy, while interoperability and data standards have yet to be harmonised.
• Regulatory gaps: Quantum simulation and predictive models have not yet been incorporated into formal regulatory review and approval processes.
Taiwan can advance the integration of quantum healthcare through three strategic approaches. First, by establishing standards for computational transparency and reproducibility to strengthen regulatory confidence.
Second, by leveraging smart healthcare regulatory sandbox mechanisms to create hospital-based pilot environments for real-world validation.
Third, by aligning with the National Health Insurance (NHI) sandbox framework to incorporate quantum simulation models into health technology assessment and reimbursement evaluation. Through the coordinated integration of hardware capabilities, algorithm development, and clinical application scenarios, Taiwan can foster a robust domestic quantum biomedical ecosystem. As quantum technologies continue to mature, early applications are expected to emerge in areas such as RNA‑based therapies for rare diseases, personalized oncology drug development, and post‑treatment risk prediction, thereby supporting the formation of a “semiconductor × biomedical × quantum” innovation ecosystem.
