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.
Why have you chosen a career in quantum?
I have always been fascinated in the proper description of macroscopic systems, especially at the juncture between quantum physics and classical physics modeling. Using machine learning and quantum-inspired models appears to be the perfect
What is a problem you dream of solving (with quantum)?
I would love to investigate the realization of a sustained nuclear fusion reaction by modeling the complex dynamics of particle interactions in a plasma together with its magnetic containment field using quantum mechanics. Equally fascinating would be modeling photosynthetic reaction centers in plants with their incredible efficiency of converting photonic energy in cascades of complex electronic excitations between macromolecules and the perfect timing of those dynamics to achieve nearly 100% conversion efficiency.
If you could meet anyone, who would it be and why?
Neil Armstrong. If I could have had the opportunity I would have liked to ask him how was he able to trust in the success of his Apollo mission, of landing successfully on the moon, of securely return to earth with his team of fellow astronauts, and so absolutely committing unwaveringly to the mission with everything that possibly could go wrong in a race to the moon that was accomplished in under ten years? How did he muster that strength, resilience and faith in the mission?