Jason Larkin
Consultant, BridgeTek professional services
Jason Larkin models and predicts how complex systems behave, from a back-of-the-envelope estimate to a full simulation. He applies that to the cost and design of AI systems in his writing for BridgeTek Labs, and to quantum and other emerging computing in his consulting work.
For seven years he was a Senior Research Scientist at Carnegie Mellon's Software Engineering Institute, where he worked on the institute's quantum research program and on DARPA-funded verified control: High-Assurance SPIRAL, which carries end-to-end guarantees through to robot and car control. Before that he was an engineer at SpiralGen, on the Spiral code-generation engine.
Since 2017 he has run his own consultancy, General Intelligence, advising startups, investors and companies on computing strategy. The work includes quantum error correction and resource estimation, quantum machine learning for a precision-medicine biotech, technical due diligence on quantum and alternative computing companies for investors, and LLM agent engineering for an AI real estate platform.
His full publication list is on Google Scholar.
Lab articles
- AI system design modeling: a tokenomics sketch
Production metrics first, then firepower, adaptation cost, and serve economics, without folding unlike numbers into one dollars-per-token headline.
BridgeTek Labs · AI system design ·
Focus areas
- AI system design and token economics
- Quantum error correction and resource estimation
- Quantum machine learning
- Technical due diligence on quantum and alternative computing
- LLM agent applications
- Physics-based modeling and simulation
Education
- Ph.D., Mechanical Engineering, Carnegie Mellon University
- M.S., Mechanical Engineering, University of Pittsburgh
- B.S., Mechanical Engineering, University of Pittsburgh
Selected publications
- Evaluation of QAOA based on the approximation ratio of individual samples. arXiv, 2020.
- Optimized quantum circuit generation with SPIRAL. IEEE High Performance Extreme Computing Conference, 2021.
- Quantum circuit optimization with SPIRAL: a first look. Supercomputing (SC), 2020.
- Projecting NISQ-era quantum advantage with QAOA. Bulletin of the American Physical Society, 2020.
- Thermal conductivity accumulation in amorphous silica and amorphous silicon. Physical Review B, 2014.
- Origins of thermal conductivity changes in strained crystals. Physical Review B, 2014.
- Predicting phonon properties from equilibrium molecular dynamics simulations. Annual Review of Heat Transfer, 2014.
- Predicting alloy vibrational mode properties using lattice dynamics calculations, molecular dynamics simulations, and the virtual crystal approximation. Journal of Applied Physics, 2013.
Work with the team
A consultation with the team about where your AI work is stuck.