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Zapata Logo


United States
< $10m
51 - 200
Open website

Zapata Computing is a quantum software company that offers computing solutions for industrial and commercial use. Its computational approaches leverage the statistical advantages of math based on quantum physics. Its primary target customers are enterprise organizations.

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Zapata is a provider of Industrial IoT analytics and modeling, networks and connectivity, platform as a service (paas), sensors, functional applications, and infrastructure as a service (iaas) technologies, and also active in the automotive, chemicals, construction and infrastructure, consumer goods, electrical grids, finance and insurance, life sciences, national security and defense, and transportation industries.
Analytics & Modeling
Machine Learning
Predictive Analytics
Utility Meters
Platform as a Service (PaaS)
Application Development Platforms
Infrastructure as a Service (IaaS)
Cloud Computing
Functional Applications
Manufacturing Execution Systems (MES)
Networks & Connectivity
Use Cases
Additive Manufacturing
Last Mile Delivery
Leasing Finance Automation
Manufacturing Process Simulation
Predictive Maintenance
Public Transportation Management
Time Sensitive Networking
Transportation Simulation
Water Utility Management
Logistics & Transportation
Product Research & Development
Quality Assurance
Construction & Infrastructure
Consumer Goods
Electrical Grids
Finance & Insurance
Life Sciences
National Security & Defense
Data Science Services
Hardware Design & Engineering Services
Testing & Certification
Zapata’s Technology Stack maps Zapata’s participation in the analytics and modeling, networks and connectivity, platform as a service (paas), sensors, functional applications, and infrastructure as a service (iaas) IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
  • Devices Layer
  • Robots
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
Number of Case Studies7
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.
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.
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.
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