The people behind the algorithms.

Dr. James LeBlanc is the scientific lead at Compute Everything, where he directs the company’s research and technical development while helping shape its broader commercialization strategy. His expertise is in developing new algorithms and computational tools for difficult problems in quantum materials and molecular simulation, spanning methods designed for both advanced classical computing and emerging quantum hardware.

James is an Associate Professor of Physics at Memorial University of Newfoundland. He received his PhD from the Guelph-Waterloo Physics Institute before completing postdoctoral research at the Max Planck Institute for the Physics of Complex Systems in Germany and the University of Michigan. He was also a member of the Simons Collaboration on the Many-Electron Problem, an international effort to establish benchmark solutions for some of the most challenging problems in quantum many-body physics.

His research spans computational many-body physics, quantum materials, high-performance scientific computing, and classical and quantum algorithm development. James has contributed to more than 90 scientific publications, with recent work increasingly focused on building general-purpose computational methods for finite-temperature physics, quantum chemistry, and materials modelling. At Compute Everything, he brings that research program together with the company’s engineering and machine-learning capabilities to develop scalable tools for materials discovery.

Ethan Armstrong leads data and machine learning, building the backend and infrastructure behind our scalable finite-temperature impurity solver. He architects the systems, data pipelines, and compute infrastructure that let the company’s core scientific engines run as a cohesive SaaS product.

Ethan’s path into machine learning and data science started in research: his graduate work applied machine learning to bacterial oligonucleotide and hydroacoustic data to detect seafloor pollution.

Ethan brings years of experience designing and implementing data products across industries including automotive, biology and risk management. His work has ranged from data products that generated millions in recurring revenue and directly contributed to a company acquisition—including an error-code-to-fix database of more than 100 million validated solutions—to production-scale predictive models, agentic AI test harnesses, and structured knowledge systems. This breadth spans the full data and AI stack, from data engineering and infrastructure design to machine learning, modern AI architectures, and deep cloud expertise. He now applies that range of skills to productizing research-grade quantum computing methods.

Dr. Bradley McNiven leads quantum research and partnerships at Compute Everything, where he develops research programs, builds collaborations, and connects the company’s technology with real-world problems in materials and molecular simulation. Working with the company’s scientific lead, he helps translate the underlying research into projects shaped around partner and industry needs.

His background spans experimental and theoretical condensed-matter physics, quantum algorithms, and materials research. His doctoral work combined quantum many-body physics with experimental studies of quantum materials, and he has since worked on quantum dynamics, quantum machine learning, and physics-informed modelling.

Brad has contributed to more than 25 peer-reviewed publications in quantum research and has extensive experience developing collaborative projects across academia, industry, and government. His current work focuses on applying the company’s methods to problems in next-generation battery materials.