Suppliers
United States
Zapata
Overview
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Zapata |
| United States | |
| Boston | |
| 2017 | |
| Public | |
| NASDAQ: ZPTA | |
| < $10m | |
| 51 - 200 | |
| Open website |
IoT Snapshot
Technology Stack
Case Studies
Number of Case Studies7
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Optimizing Materials Discovery with Quantum and Classical Machine Learning Techniques
BASF, the world's largest chemical producer, is constantly innovating and developing new materials for various sectors including consumer goods, transportation, healthcare, agriculture, and energy. The challenge lies in their pursuit of sustainable and innovative new materials. BASF is keen on exploring how AI and quantum techniques can be utilized on today's classical computers to enhance existing cheminformatics solutions. Specifically, they are interested in machine learning models that can predict the molecular properties of new materials. The goal is to leverage these advanced technologies to boost their materials discovery process and make it more efficient and effective. |
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Quantum Chemistry Revolution: bp and Zapata's Quantum Computing Collaboration
bp, one of the world's largest energy companies, is constantly seeking innovative ways to leverage emerging technologies. One such technology is quantum computing, which has the potential to disrupt industries that rely heavily on chemistry. Quantum computing theoretically has the ability to simulate molecules and predict their properties beyond the capabilities of classical computers. It also has implications for various business operations such as logistics, manufacturing, finance, security, and more. However, the extent and timeline of this disruption remain uncertain. To better prepare for the quantum future and gain a competitive edge, bp is exploring the impact of quantum computing on chemistry calculations within and beyond their core business. |
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Optimizing Automotive Manufacturing with Industrial Generative AI
Global manufacturers like BMW face a complex optimization problem: scheduling their workers to achieve production targets while minimizing idle hours. The challenge lies in the wide range of possible configurations and numerous constraints. Different shops have varying production rates, and each has their own discrete set of shift schedules. Furthermore, manufacturers need to prevent overflows and shortages in the buffers between steps in the manufacturing process. The complexity of the problem is further compounded by the need to optimize across multiple variables and constraints, making it a difficult task to solve using traditional methods. |
