internship program.

Intern with Zapata AI.

Our scientists and engineers are accelerating the quantum future and pioneering new quantum, generative AI, and machine learning algorithms and techniques to tackle the most complex problems in industry.

Our mindset

We are creative, curious, and revolutionary.

We are committed to creating an environment that accelerates our teammates’ growth and positive impact in the world. We are fast-paced, highly collaborative, and cross-disciplinary. Our world-class team of resourceful problem solvers is pioneering commercial algorithms, research, and product development.

About Zapata AI
Intern Highlight

Amara Katabarwa

When Amara Katabarwa first joined Zapata AI as a summer 2018 research intern, he was studying the characterization of noise in quantum devices as a Ph.D. student at University of Georgia with supervisor, Michael Geller. Amara brought his expertise to Zapata AI, where he worked on simulating errors in quantum devices using variational quantum channels as well as developing methods to improve correction of readout errors. After completing his Ph.D., Amara joined the company full-time as a Quantum Research Scientist, leading and contributing to numerous projects, including Geometry of Two-Qubit PQCs , experimental demonstrations of Variational Quantum Factoring, quantum generative modeling, and Robust Amplitude Estimation. Today, Amara leads the hardware integration team.

Amara's Bio

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Program History

Previous Zapata AI internship classes

2023

2022

2021

2020

2019

2018

2023

2023

Contributions include:
  • Development of GenAI demos for customers
  • Extension of research and product capabilities for LLM Applications
  • Performance improvements to proprietary QML libraries
  • Development of benchq software for modeling fault-tolerant quantum computations
  • Development of quantum compilation software for differential equations quantum algorithms
  • Integration of MLflow with Orquestra for data management and visualization

2022

2022

Contributions include:
  • Developed an integration with Nvidia’s GPU-based quantum simulator, cuQuantum
  • Developed tools that allow us to better use NISQ devices
  • Demonstrated generalization capabilities of quantum circuit Born machines (QCBMs)
  • Enhanced performance of probabilistic models via data-model matching
  • Created documentation for Orquestra and insightful blog posts 
  • Boosted optimization capabilities of tensor-network-based generative models for constrained and continuous problems

2021

2021

Contributions include:

2020

2020

Contributions include:

 

2019

2019

Contributions include:
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