Introduction
In June 2022, Covestro, one of the world’s leading polymer companies, and QC Ware, a quantum software and services company, announced a five-year collaboration agreement to develop quantum algorithms for materials research and development. This partnership aimed to prepare Covestro to fully deploy quantum algorithms for the discovery of new materials and catalysts on near-term quantum hardware . PrnewswireHPCwire
The Materials Science Challenge
Covestro faced significant computational challenges in its research and development of new materials and more efficient production processes. As a major polymer manufacturer with 2020 sales of €10.7 billion and 33 production sites worldwide, the company was committed to developing sustainable solutions and transitioning to a circular economy . Prnewswire
Traditional computational methods for simulating molecular behavior had reached their limits when it came to modeling complex chemical systems relevant to industrial applications. Simulating large-scale molecules and their properties—such as forces on atoms, light absorption, or electrical conductivity—required computational resources beyond what classical computers could efficiently provide . The Quantum Insider
Additionally, Covestro needed to accelerate its R&D processes to support the company’s transformation toward carbon neutrality and a circular economy. Developing sustainable polymer materials for industries such as automotive, construction, electronics, and household appliances required more powerful computational tools to discover and optimize new materials with specific properties . Prnewswire
Quantum Solution
The QC Ware-Covestro partnership developed advanced quantum algorithms specifically designed for materials science applications with several key innovations:
Quantum Chemistry Simulation
The collaboration focused on creating quantum algorithms that could accurately model the behavior of complex molecules relevant to Covestro’s business. These algorithms were designed to simulate quantum-scale interactions more naturally and efficiently than classical computing approaches, potentially enabling the discovery of new materials and catalysts . HPCwirePrnewswire
A distinctive feature of their approach was the ability to compute not just ground state energies of molecules—which had been the focus of most quantum chemistry research—but also crucial properties such as forces on atoms, color and light-absorbing properties, and electrical conductivity. These capabilities were critical for providing practical value to computational chemists in industrial settings . The Quantum Insider
Resource-Efficient Quantum Techniques
The team developed new quantum techniques that significantly reduced the quantum computing resources required to design new materials and chemical processes. These innovations included reductions in both circuit depth and connectivity requirements—critical components for implementing practical applications on near-term quantum hardware with limited capabilities . The Quantum Insider
Hybrid Quantum-Classical Architecture
Recognizing that practical applications required leveraging both quantum and classical computing strengths, the partners created a full-stack solution that merged high-performance classical techniques for pre- and post-processing with advanced quantum algorithms reserved for the most computationally challenging aspects of the problems . The Quantum Insider
This hybrid approach allowed them to tackle larger molecular systems than would be possible with purely quantum approaches on current hardware . Quantumcomputingreport
Implementation
The implementation of this quantum solution involved a structured approach that began with a proof-of-concept project and evolved into a long-term partnership.
Initial Proof-of-Concept Project
Before signing the five-year agreement, Covestro and QC Ware conducted a year-long collaboration on a proof-of-concept project that explored modeling large-scale molecules needed for industrial applications on near-term quantum computers. This project allowed them to validate their approach and establish a foundation for more extensive collaboration . HPCwirePrnewswire
The results of this initial project were documented in two research papers:

“Local, Expressive, Quantum-Number-Preserving VQE Ansatze for Fermionic Systems” published in New Journal of Physics
“Analytical Ground- and Excited-State Gradients for Molecular Electronic Structure Theory from Hybrid Quantum/Classical Methods” published on arXiv . PrnewswireThe Quantum Insider

These papers introduced new quantum techniques for simulating molecular systems with significantly reduced resource requirements, creating a pathway to practical applications on near-term quantum hardware . The Quantum Insider
Cross-Disciplinary Team Formation
The implementation involved creating a cross-disciplinary team that combined Covestro’s expertise in computational chemistry and manufacturing with QC Ware’s specialized knowledge in quantum algorithms. Christian Gogolin, Expert Advanced Computational Concepts and Quantum Computing at Covestro Digital R&D, led efforts on the Covestro side, while Robert Parrish, Head of Chemistry Simulations at QC Ware, spearheaded the quantum algorithm development . The Quantum Insider
This collaborative structure enabled the partners to bridge the gap between theoretical quantum computing capabilities and practical industrial applications . HPCwirePrnewswire
Strategic Investment
In September 2021, Covestro deepened its commitment to quantum computing by co-leading QC Ware’s $25 million Series B funding round alongside Koch Disruptive Technologies. This investment reflected Covestro’s belief in the strategic importance of quantum computing for its future R&D capabilities and provided additional resources to accelerate the development of quantum algorithms for materials discovery . Prnewswire
Results and Business Impact
The partnership between QC Ware and Covestro delivered several significant outcomes with implications for the future of materials science and polymer development.
Enhanced Computational Capabilities
The quantum algorithms developed through the collaboration demonstrated the potential to solve simulation problems that were “out of the reach of state-of-the-art classical computing,” according to Torsten Heinemann, Head of Group Innovation at Covestro . HPCwire
The approach allowed for the accurate prediction of not just molecular energies but also forces and other properties critical for materials development. This capability represented a significant advancement over previous quantum simulation approaches, providing Covestro’s researchers with deeper insights into molecular behavior . The Quantum Insider
Path to Quantum Advantage
While the full benefits of quantum computing for materials science would require more powerful quantum hardware, the partnership established a clear trajectory toward practical quantum advantage. The companies believed their algorithms would achieve quantum advantage—solving real-world problems better than classical computers—once processors with 200-500 qubits became available . Quantumcomputingreport
This positioned Covestro to be ready to leverage quantum computing for competitive advantage as soon as the hardware matured sufficiently . Prnewswire
Accelerated R&D Processes
Torsten Heinemann noted that Covestro’s hope was that “this work will help take our R&D efforts to a completely new level and accelerate the pace at which we can develop innovations” . HPCwire
By providing new computational tools for materials discovery, the quantum algorithms could potentially reduce the time and resources required to develop new products, enabling more rapid innovation in sustainable materials . Prnewswire
Future Directions
The five-year collaboration agreement between QC Ware and Covestro outlined several directions for future development of quantum applications in materials science.
Expanding Materials Discovery Applications
The partners planned to use their quantum algorithms to discover new material classes and develop more efficient, resource-conserving production processes. This would support Covestro’s strategic goal of enhancing its digital R&D processes to achieve carbon neutrality through circular economy approaches . HPCwireThe Quantum Insider
Scaling to Larger Molecular Systems
As quantum hardware capabilities continued to improve, the partners intended to scale their algorithms to tackle increasingly complex molecular systems. Their approach was designed to be adaptable to growing qubit counts and improving coherence times, allowing them to address more industrially relevant molecules as the technology matured . Quantumcomputingreport
Integration with R&D Workflows
A key goal was to develop quantum computing tools that Covestro’s R&D team could integrate into their regular workflows. This would involve creating user-friendly interfaces and connecting quantum capabilities with existing computational chemistry infrastructure to make quantum computing accessible to researchers without specialized quantum expertise . HPCwire
Industry Standard Setting
As a pioneering partnership in quantum computing for materials science, QC Ware and Covestro positioned themselves to help establish industry standards and best practices in this emerging field. Their research papers and implementations could guide other companies exploring quantum applications in chemistry and materials science . Fourester Research
References

QC Ware. (2022). “Quantum Algorithms for Materials Science.”
Covestro Digital R&D. (2022). “Quantum Computing for Polymer Innovation.”
New Journal of Physics. (2021). “Local, Expressive, Quantum-Number-Preserving VQE Ansatze for Fermionic Systems.”
arXiv. (2021). “Analytical Ground- and Excited-State Gradients for Molecular Electronic Structure Theory from Hybrid Quantum/Classical Methods.”