98 M C K I N S EY Q UA RT E R LY Quantum’s Promise - - - - - - - - QC applications with classical high-performance computing and AI. Practical use cases include sim ulating complex molecules and materials, which has gained traction in pharmaceuticals and chemicals; optimizing financial portfolios; and modeling com plex supply chains or energy grid loads, where even small improvements can deliver significant value. Such QC pilots are most often deployed alongside classical computers. In these cases, classical com puters process high-volume calculations, while QC machines solve the knottiest computations. Even a hybrid approach promises to dramatically boost computing speed, which our analysis shows could translate into billions of dollars in value for large organizations. QC companies claim that quantum computers can outperform classical computers; now they must prove that this performance can translate into measurable business value. Stakeholders will need to drive QC from a theoretical promise to a foundational element of the next computational era. The Five-to-Ten-Year Horizon: Full-scale Impact from Fault-Tolerant QC Recent product road maps suggest that a new set of technological advances is likely to enable fault-tolerant quantum computing (FTQC) by 2030. This breakthrough will feature automatic error correction and stable qubits. Coupled with more advanced algorithms, FTQC machines will enable applications such as large-scale simulation for complex biology, climate, and materials mod eling; deep integration of QC into mission-critical optimization and risk engines; and advanced AI that can spot patterns in high-dimensional data for yet-undiscovered applications. Pairing QC with AI will be critical. For example, quantum machine learning (QML) is already speeding up some of the heavy math and optimization steps that make AI model training so resource intensive today, while quantum circuits could allow smaller, lower-cost AI models to perform much more efficiently. These technological gains signal an upcoming convergence between AI and QC and could be the breakthrough that propels the long-term value cre ation from QC that executives seek. QUANTUM’S KEY STAKEHOLDERS Our analysis identifies three main stakeholder groups—users, investors, and technology provid ers—that will be at the forefront of transforming QC from a theoretical promise to a foundational element of the next computational era. Users Our research shows that hundreds of organizations worldwide are already engaging with QC. Activity among sectors, however, remains uneven. We see an “urgency paradox” in quantum adoption. While sec tors such as pharmaceuticals and chemicals have the most promising problem sets that could be solved by QC, other sectors are moving faster, such as defense, finance, and telecommunications (Exhibit 2). These industries are still operating from a risk perspec tive, where the cost of being second outweighs the technology’s uncertainty. Nonetheless, today’s early efforts can guide leaders just starting to explore how QC could affect their businesses, informing them on what’s possible in the near to medium term. Today, QC users predominantly include sectors that need to solve complex, high-value problems, such as pharmaceuticals. They are working with QC companies to pilot targeted use cases. For example, Amgen teamed up with Quantinuum to study peptide binding, and Biogen has worked with 1QBit to accelerate comparisons of molecules for neurological disorders such as Alzheimer’s and Parkinson’s diseases. Financial institutions are using QC to enhance portfolio optimization and risk assessment. For example, several financial institutions, including BBVA and Crédit Agricole CIB, have partnered with