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Mark Jack

Quantum Application Scientist, Zapata Computing

Mark Jack has 20+ years of expertise in quantum physics modeling and has gained several years of experience using high-performance computing as academic researcher with a PhD in theoretical physics. As data scientist and machine learning engineer his professional experience includes 4+ years of hands-on project work in building machine learning and deep-learning applications for business use cases in digital marketing, sales, the financial services sector and logistics with work in startups and in the consulting industry. Machine learning projects included for example the use of natural language programming, speech recognition, content generation, customer segmentation, lead generation and agent matching in sales. He has built machine learning and deep learning solutions in Python and with PyTorch and successfully deployed several solutions as containerized applications in the cloud or on a cluster following agile software development practices. In quantum computing, he for example successfully deployed use cases around route optimization as containerized applications in the cloud via combinatorial optimization on quantum computing hardware. He also created a simplified quantum neural network model as a demonstration of a quantum machine learning workflow for a hybrid solution using both classical neural network layers and a quantum circuit layer for a simplified classification task. He has worked with a number of different quantum software frameworks such as IBM Qiskit, D-Wave Ocean SDK and PennyLane and has successfully employed these on QC and QML related projects.

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