Introduction
The partnership between BP and Zapata Computing represents a significant step in applying quantum computing to real-world energy sector challenges. BP, one of the world’s largest integrated oil and gas companies, recognized the potential of quantum computing to solve complex optimization problems that are computationally intractable with classical computers. Zapata Computing, a leading enterprise quantum software company, brought its expertise in quantum algorithm development and its Orquestra platform to the partnership. The collaboration aimed to explore multiple use cases where quantum computing could provide competitive advantages in the energy sector. These included optimizing supply chain logistics, improving computational chemistry simulations for new materials and catalysts, and enhancing portfolio optimization for trading operations. The partnership leveraged Zapata’s quantum-ready software platform to develop and test quantum algorithms on both quantum simulators and actual quantum hardware from various providers. This collaboration exemplified how major energy companies are investing in quantum computing research to prepare for the quantum advantage era, where quantum computers will outperform classical computers for specific problem types relevant to their business operations.
Challenge
BP faced several computational challenges that traditional computing methods struggled to address efficiently. In the oil and gas industry, optimization problems grow exponentially complex as variables increase. Supply chain optimization, for instance, involves coordinating thousands of assets, routes, and timing constraints across global operations. Classical computers require prohibitive amounts of time to find optimal solutions for such large-scale problems. Additionally, BP’s research into new materials for carbon capture and energy storage demanded accurate molecular simulations. Classical computational chemistry methods face limitations when modeling quantum mechanical effects in complex molecular systems. The company also dealt with portfolio optimization challenges in energy trading, where finding the optimal mix of investments while managing risk across volatile markets becomes computationally intensive. Furthermore, BP needed to explore quantum computing capabilities while the technology was still in its early stages, requiring a partner who could bridge the gap between current quantum hardware limitations and practical business applications. The challenge extended beyond just technical feasibility to include developing quantum literacy within BP’s organization and creating frameworks for evaluating quantum advantage for specific use cases. BP required a systematic approach to identify which problems would benefit most from quantum computing and how to prepare their data and workflows for the quantum era.
Solution
Zapata Computing provided BP with access to its Orquestra platform, a quantum-ready workflow management system that enabled BP to experiment with quantum algorithms across different quantum hardware backends and simulators. The solution involved developing hybrid classical-quantum algorithms tailored to BP’s specific optimization challenges. For supply chain optimization, Zapata implemented Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) variants adapted for logistics problems. These algorithms could handle the complex constraints and variables in BP’s supply chain while working within the limitations of near-term quantum devices. For molecular simulation applications, Zapata developed quantum chemistry algorithms that could model molecular interactions more accurately than classical methods, particularly for systems with strong electron correlation effects. The team created custom ansätze (trial wavefunctions) optimized for the specific molecules relevant to BP’s research in catalysts and materials. Zapata’s platform allowed BP to run these algorithms on multiple quantum backends, including IBM Quantum, Honeywell (now Quantinuum), and IonQ systems, as well as on powerful classical simulators for benchmarking. The solution included tools for comparing quantum and classical algorithm performance, helping BP identify the threshold where quantum advantage might emerge. Additionally, Zapata provided quantum algorithm consulting and training to BP’s technical teams, building internal quantum computing capabilities.
Implementation
The implementation began with a comprehensive assessment phase where Zapata’s quantum scientists worked closely with BP’s domain experts to identify and prioritize use cases. This involved translating BP’s business problems into mathematical formulations suitable for quantum algorithms. The teams established benchmarking protocols to compare quantum approaches against best-in-class classical algorithms. For the supply chain optimization workstream, the implementation involved mapping BP’s logistics network into quantum-compatible graph problems. The team developed custom preprocessing techniques to reduce problem sizes to fit on available quantum hardware while preserving essential problem features. They implemented error mitigation strategies to improve results from noisy intermediate-scale quantum (NISQ) devices. In the molecular simulation track, implementation required developing efficient mappings from molecular orbital representations to qubit states. The team created automated workflows in Orquestra that could take molecular structures as input and generate optimized quantum circuits for energy calculations. Regular testing cycles were established, running algorithms on both quantum hardware and simulators to track performance improvements as quantum hardware evolved. BP’s teams received hands-on training through workshops and collaborative development sessions. The implementation included creating documentation and best practices guides specific to BP’s use cases, ensuring knowledge transfer and sustainable quantum computing capabilities within the organization.
Results and Business Impact
The partnership yielded significant insights into quantum computing’s potential for energy sector applications. While full quantum advantage was not achieved due to current hardware limitations, the collaboration established clear pathways and benchmarks for future quantum implementations. In supply chain optimization, the quantum algorithms demonstrated the ability to find high-quality solutions for simplified versions of BP’s logistics problems, with performance improving as quantum hardware quality increased. The team identified specific problem characteristics where quantum approaches showed promise of outperforming classical methods as hardware scales. For molecular simulations, quantum algorithms successfully calculated ground state energies for small molecules relevant to catalyst design with chemical accuracy on quantum simulators. This validated the approach and provided a roadmap for tackling larger, more industrially relevant molecules as quantum computers improve. The partnership generated valuable intellectual property, including novel quantum algorithm variants optimized for energy sector problems. BP developed internal quantum computing expertise, with several team members becoming proficient in quantum algorithm development and implementation. The collaboration also helped BP make informed decisions about quantum computing investments and identify key hardware specifications needed for their use cases. From a strategic perspective, BP positioned itself as an early adopter in quantum computing for energy, potentially gaining first-mover advantages as the technology matures.
Future Directions
The partnership established a foundation for continued quantum computing research and development at BP. Future directions include scaling up the algorithms as quantum hardware improves, with specific focus on achieving quantum advantage for select optimization problems within the next 5-10 years. BP plans to continue monitoring quantum hardware developments and maintain relationships with multiple quantum computing providers through platforms like Orquestra. The molecular simulation work will expand to include larger molecular systems relevant to carbon capture technologies and advanced materials for renewable energy applications. BP is exploring additional use cases including seismic data processing, reservoir simulation, and financial portfolio optimization. The company is investing in building a larger internal quantum computing team and establishing partnerships with academic institutions to advance quantum algorithm research. There are plans to develop industry-specific quantum computing benchmarks and contribute to quantum computing standards for the energy sector. BP is also investigating the potential of quantum machine learning for pattern recognition in geological data and predictive maintenance applications. The long-term vision includes establishing quantum computing as a standard tool in BP’s computational toolkit, ready to deploy as quantum hardware reaches sufficient scale and reliability for production workloads.