Solving chemistry’s toughest problems: The quantum computing advantage
Summarize this article with:
Solving chemistry’s toughest problems: The quantum computing advantageFebruary 19, 2026 | Article Henning Soller Anna HeidQuantum computing is poised to substantially improve computational capabilities in the chemicals value chain. Preparing now could help companies gain a significant competitive edge. (5 pages) The chemicals industry is navigating a period of profound change. Shifting end-market dynamics—driven by digitalization, the rise of electric vehicles, and the transition to renewable energy—are creating demand for novel materials and more sustainable solutions. At the same time, stricter environmental regulations, such as those targeting per- and polyfluoroalkyl substances (PFAS), are pushing companies to develop safer, less-hazardous formulations. Adding to these challenges is the sharp rise in global energy prices, which has made the development of more energy-efficient chemical processes and better catalysts a top priority for the industry. Compounding these challenges are evolving trade policies, overcapacity, and potential imbalances in supply and demand. In this mature industry, where tight budgets and a historical focus on incremental improvements often constrain innovation, the need for bold, transformative solutions has never been greater. Quantum computing: A game-changing technology Quantum computing (QC) is a powerful new tool to address the chemicals industry’s most pressing challenges. By performing calculations that surpass the capabilities of even the most advanced supercomputers, QC enables breakthroughs in computing and analytics that can significantly accelerate innovation and optimize operations. Early estimates suggest that the total potential value of QC applications in chemicals could be from $200 billion to $500 billion by 2035. This value would derive from cost reductions, faster R&D, and new products and revenue streams. Classical computing struggles to model the complex quantum behavior of electrons and atoms at the heart of chemical reactions. QC, however, is uniquely suited for this task. Several quantum algorithm classes are particularly promising: Quantum simulations accurately capture quantum mechanical effects in molecular systems and materials. Hybrid AI/ML models leverage quantum-generated data to enhance machine learning (ML) predictions, especially where data sets are sparse or noisy. Quantum optimization algorithms are expected to solve production, logistics, and process scheduling problems that are computationally intractable for classical systems, though their advantages are yet to be confirmed. In the near term, hybrid approaches that combine classical high-performance computing (HPC), AI/ML, and quantum tools will likely deliver the greatest value. Computational methods, such as density functional theory, combined with HPC, already deliver strong results for many chemical problems but struggle with systems involving highly correlated electrons, excited states, and non-equilibrium dynamics. Quantum computing is expected to complement and extend these approaches, bringing capabilities that classical methods lack. R&D workflows will increasingly become hybrid: Classical computing will continue to handle equilibrium and large-scale simulations, while QC will focus on non-equilibrium dynamics, reaction kinetics, and catalytic behaviors that currently defy accurate modeling. This hybrid approach will allow researchers to decompose problems strategically, assigning the right computational tool to each component, maximizing efficiency and insight. Reimagining R&D: A new era in molecular science Quantum computing has the potential to fundamentally reshape chemical R&D. High-precision quantum molecular simulations will enable researchers to model complex molecules and reactions with unprecedented accuracy, speeding up the design of novel materials, advanced formulations, and high-performance catalysts. QC can also deepen our understanding of known materials, enabling improved formulation design, toxicity analysis, and long-term durability predictions. Simulating the environmental behavior and degradation pathways of chemicals—currently a costly and time-intensive process—could become more accurate and efficient. A key limitation today is that simulating chemical reactions—especially those far from equilibrium—is almost always impossible with classical computers. Quantum computing could, for the first time, enable accurate modeling of these non-equilibrium processes, unlocking new insights into reaction kinetics, catalysis, and material behavior. Real-world examples are already emerging: BP, for instance, is working with ORCA Computing to apply hybrid quantum–classical machine learning to generative modeling of molecular conformations.1William Clements, Corneliu Buda, Claudia Perry, and Peter Lemke, “bp and ORCA Computing team up to explore quantum-powered innovation in computational chemistry,” ORCA Computing, June 6, 2024.
Mitsubishi Chemical Group is collaborating with PsiQuantum to simulate excited states of photochromic molecules used for energy-efficient data storage, smart windows, solar energy storage, and other photoswitching applications.2“PsiQuantum, Mitsubishi UFJ Financial Group, and Mitsubishi Chemical announce partnership to design energy-efficient materials on PsiQuantum’s fault-tolerant quantum computer,” PsiQuantum press release, January 24, 2024. These efforts highlight QC’s role in reducing reliance on physical synthesis and testing, shortening innovation cycles and lowering R&D costs. Other promising applications include: designing next-generation biocides for sustainable agriculture developing higher-energy-density battery materials for electric vehicles creating novel organic light-emitting diode (OLED) luminophores for advanced display technologies simulating more-efficient absorbents for direct air capture of carbon dioxide improving perovskite stability for solar energy inventing peptides that can break down microplastics Transforming manufacturing: Boosting efficiency and driving sustainability Quantum computing can also improve manufacturing. By modeling chemical reactions and process dynamics with greater accuracy, QC can help increase yields, improve energy inputs, and reduce material waste BASF is exploring quantum applications across its operations, working with SEEQC to model industrial-scale chemical reactions3“SEEQC partners with BASF to explore applications of quantum computing in chemical reactions for industrial use,” SEEQC press release, February 9, 2023. and with Kipu Quantum to optimize complex logistics and supply chain challenges.4“Kipu Quantum’s algorithms for BASF logistics optimization,” Kipu Quantum press release, July 25, 2024. These efforts aim to improve efficiency and responsiveness throughout the production network. As quantum hardware matures, process simulations will become increasingly accurate, enabling tighter control over key parameters, more sustainable processes, and lower costs. Charting the course: The evolving quantum landscape in chemicals While still in its early stages, quantum applications in the chemicals industry are advancing rapidly. Leading chemicals companies and their downstream customers, such as automotive companies, are actively investing in QC and exploring its potential. For example, Volkswagen is working with IQM to simulate next-generation battery materials using hybrid quantum–classical methods.5“Volkswagen and IQM Quantum Computers release study on battery simulation,” IQM press release, November 15, 2024. Hyundai has partnered with IonQ to explore quantum applications in machine vision for future vehicles, lithium compound modeling, and catalytic chemistry.6“IonQ and Hyundai Motor Company expand quantum computing partnership, continuing pursuit of automotive innovation,” IonQ press release, December 6, 2022. Meanwhile, Covestro and Google Quantum AI are developing advanced error-mitigation strategies to push the limits of today’s quantum devices, laying the groundwork for more reliable near-term simulations.7Christian Gogolin, Ryan Babbush, and Thomas O’Brien, “Google and Covestro push the boundaries of near-term quantum computing,” Covestro, October 16, 2023. While fully fault-tolerant quantum computers remain under development, road maps suggest that increasingly powerful systems will emerge within the next two to five years. These advancements are expected to bring practical applications and tangible benefits to the chemicals industry. Preparing for the quantum era: Strategic steps for chemicals companies For chemicals companies, the time to engage with quantum computing is now. Early adopters can secure advantages in technology leadership, cybersecurity readiness, and workforce capability. Building the necessary technical capabilities and integrating QC into workflows will require time and strategic planning. Key steps include: Establish quantum-ready IT infrastructure, including access to cloud-based QC platforms. Develop proprietary algorithms tailored to high-impact use cases. Form strategic partnerships with leading quantum technology providers. Address cyber risks by implementing post-quantum cryptography. Recruit and upskill talent in quantum information science, algorithm development, and hybrid computing. By integrating QC with AI, HPC, and other digital technologies, companies can build a flexible, modular computing architecture—one that applies the best tool to each problem. This approach will enable companies to realize the full potential of quantum and digital transformation in tandem. Quantum computing is not a silver bullet, but it is a foundational shift in what is computationally possible. For chemicals companies, the implications are vast: accelerated discovery, leaner production, and the ability to solve previously intractable problems. By starting now, industry leaders can stay ahead of the curve and help shape the next era of innovation in chemistry.Henning Soller is a partner in McKinsey’s Frankfurt office, and Anna Heid is an associate partner in the Zurich office. The authors wish to thank Alex Zhang, Christof Witte, Michael Bogobowicz, Obi Ezekoye, and Ulrich Weihe for their contributions to this article.Explore a career with usRelated ArticlesReportThe Year of Quantum: From concept to reality in 2025Podcast - The McKinsey PodcastQuantum computing: Game onArticleThe quantum revolution in pharma: Faster, smarter, and more precise (5 pages) The chemicals industry is navigating a period of profound change. Shifting end-market dynamics—driven by digitalization, the rise of electric vehicles, and the transition to renewable energy—are creating demand for novel materials and more sustainable solutions. At the same time, stricter environmental regulations, such as those targeting per- and polyfluoroalkyl substances (PFAS), are pushing companies to develop safer, less-hazardous formulations. Adding to these challenges is the sharp rise in global energy prices, which has made the development of more energy-efficient chemical processes and better catalysts a top priority for the industry. Compounding these challenges are evolving trade policies, overcapacity, and potential imbalances in supply and demand. In this mature industry, where tight budgets and a historical focus on incremental improvements often constrain innovation, the need for bold, transformative solutions has never been greater. Quantum computing: A game-changing technology Quantum computing (QC) is a powerful new tool to address the chemicals industry’s most pressing challenges. By performing calculations that surpass the capabilities of even the most advanced supercomputers, QC enables breakthroughs in computing and analytics that can significantly accelerate innovation and optimize operations. Early estimates suggest that the total potential value of QC applications in chemicals could be from $200 billion to $500 billion by 2035. This value would derive from cost reductions, faster R&D, and new products and revenue streams. Classical computing struggles to model the complex quantum behavior of electrons and atoms at the heart of chemical reactions. QC, however, is uniquely suited for this task. Several quantum algorithm classes are particularly promising: Quantum simulations accurately capture quantum mechanical effects in molecular systems and materials. Hybrid AI/ML models leverage quantum-generated data to enhance machine learning (ML) predictions, especially where data sets are sparse or noisy. Quantum optimization algorithms are expected to solve production, logistics, and process scheduling problems that are computationally intractable for classical systems, though their advantages are yet to be confirmed. In the near term, hybrid approaches that combine classical high-performance computing (HPC), AI/ML, and quantum tools will likely deliver the greatest value. Computational methods, such as density functional theory, combined with HPC, already deliver strong results for many chemical problems but struggle with systems involving highly correlated electrons, excited states, and non-equilibrium dynamics. Quantum computing is expected to complement and extend these approaches, bringing capabilities that classical methods lack. R&D workflows will increasingly become hybrid: Classical computing will continue to handle equilibrium and large-scale simulations, while QC will focus on non-equilibrium dynamics, reaction kinetics, and catalytic behaviors that currently defy accurate modeling. This hybrid approach will allow researchers to decompose problems strategically, assigning the right computational tool to each component, maximizing efficiency and insight. Reimagining R&D: A new era in molecular science Quantum computing has the potential to fundamentally reshape chemical R&D. High-precision quantum molecular simulations will enable researchers to model complex molecules and reactions with unprecedented accuracy, speeding up the design of novel materials, advanced formulations, and high-performance catalysts. QC can also deepen our understanding of known materials, enabling improved formulation design, toxicity analysis, and long-term durability predictions. Simulating the environmental behavior and degradation pathways of chemicals—currently a costly and time-intensive process—could become more accurate and efficient. A key limitation today is that simulating chemical reactions—especially those far from equilibrium—is almost always impossible with classical computers. Quantum computing could, for the first time, enable accurate modeling of these non-equilibrium processes, unlocking new insights into reaction kinetics, catalysis, and material behavior. Real-world examples are already emerging: BP, for instance, is working with ORCA Computing to apply hybrid quantum–classical machine learning to generative modeling of molecular conformations.1William Clements, Corneliu Buda, Claudia Perry, and Peter Lemke, “bp and ORCA Computing team up to explore quantum-powered innovation in computational chemistry,” ORCA Computing, June 6, 2024.
Mitsubishi Chemical Group is collaborating with PsiQuantum to simulate excited states of photochromic molecules used for energy-efficient data storage, smart windows, solar energy storage, and other photoswitching applications.2“PsiQuantum, Mitsubishi UFJ Financial Group, and Mitsubishi Chemical announce partnership to design energy-efficient materials on PsiQuantum’s fault-tolerant quantum computer,” PsiQuantum press release, January 24, 2024. These efforts highlight QC’s role in reducing reliance on physical synthesis and testing, shortening innovation cycles and lowering R&D costs. Other promising applications include: designing next-generation biocides for sustainable agriculture developing higher-energy-density battery materials for electric vehicles creating novel organic light-emitting diode (OLED) luminophores for advanced display technologies simulating more-efficient absorbents for direct air capture of carbon dioxide improving perovskite stability for solar energy inventing peptides that can break down microplastics Transforming manufacturing: Boosting efficiency and driving sustainability Quantum computing can also improve manufacturing. By modeling chemical reactions and process dynamics with greater accuracy, QC can help increase yields, improve energy inputs, and reduce material waste BASF is exploring quantum applications across its operations, working with SEEQC to model industrial-scale chemical reactions3“SEEQC partners with BASF to explore applications of quantum computing in chemical reactions for industrial use,” SEEQC press release, February 9, 2023. and with Kipu Quantum to optimize complex logistics and supply chain challenges.4“Kipu Quantum’s algorithms for BASF logistics optimization,” Kipu Quantum press release, July 25, 2024. These efforts aim to improve efficiency and responsiveness throughout the production network. As quantum hardware matures, process simulations will become increasingly accurate, enabling tighter control over key parameters, more sustainable processes, and lower costs. Charting the course: The evolving quantum landscape in chemicals While still in its early stages, quantum applications in the chemicals industry are advancing rapidly. Leading chemicals companies and their downstream customers, such as automotive companies, are actively investing in QC and exploring its potential. For example, Volkswagen is working with IQM to simulate next-generation battery materials using hybrid quantum–classical methods.5“Volkswagen and IQM Quantum Computers release study on battery simulation,” IQM press release, November 15, 2024. Hyundai has partnered with IonQ to explore quantum applications in machine vision for future vehicles, lithium compound modeling, and catalytic chemistry.6“IonQ and Hyundai Motor Company expand quantum computing partnership, continuing pursuit of automotive innovation,” IonQ press release, December 6, 2022. Meanwhile, Covestro and Google Quantum AI are developing advanced error-mitigation strategies to push the limits of today’s quantum devices, laying the groundwork for more reliable near-term simulations.7Christian Gogolin, Ryan Babbush, and Thomas O’Brien, “Google and Covestro push the boundaries of near-term quantum computing,” Covestro, October 16, 2023. While fully fault-tolerant quantum computers remain under development, road maps suggest that increasingly powerful systems will emerge within the next two to five years. These advancements are expected to bring practical applications and tangible benefits to the chemicals industry. Preparing for the quantum era: Strategic steps for chemicals companies For chemicals companies, the time to engage with quantum computing is now. Early adopters can secure advantages in technology leadership, cybersecurity readiness, and workforce capability. Building the necessary technical capabilities and integrating QC into workflows will require time and strategic planning. Key steps include: Establish quantum-ready IT infrastructure, including access to cloud-based QC platforms. Develop proprietary algorithms tailored to high-impact use cases. Form strategic partnerships with leading quantum technology providers. Address cyber risks by implementing post-quantum cryptography. Recruit and upskill talent in quantum information science, algorithm development, and hybrid computing. By integrating QC with AI, HPC, and other digital technologies, companies can build a flexible, modular computing architecture—one that applies the best tool to each problem. This approach will enable companies to realize the full potential of quantum and digital transformation in tandem. Quantum computing is not a silver bullet, but it is a foundational shift in what is computationally possible. For chemicals companies, the implications are vast: accelerated discovery, leaner production, and the ability to solve previously intractable problems. By starting now, industry leaders can stay ahead of the curve and help shape the next era of innovation in chemistry.Henning Soller is a partner in McKinsey’s Frankfurt office, and Anna Heid is an associate partner in the Zurich office. The authors wish to thank Alex Zhang, Christof Witte, Michael Bogobowicz, Obi Ezekoye, and Ulrich Weihe for their contributions to this article.Explore a career with us