2026-08-14 –, Room 5
Harmoniqs is a startup building quantum control infrastructure entirely in Julia. Our open-source stack, Piccolo.jl, and its private extension Piccolissimo.jl share one language from API to numerics. We discuss compiling and delivering Julia without exposing source code, deploying on HPC resources including GPU clusters, and how Julia's single-language design makes AI coding agents unusually effective for a small team. A case study in why Julia is ready for startups.
Harmoniqs builds control software infrastructure for quantum computing. Our entire stack is Julia, from the open-source Piccolo.jl framework for quantum optimal control to Piccolissimo.jl, a private extension providing specialized integrators, operators, and constraints for production workloads.
Why Julia for a startup? The conventional wisdom says startups should use Python for speed-to-market and C++ for speed-to-solution. Julia gives us both. The same code a researcher writes to prototype a new integrator is the code that ships to customers. No rewrite step, no glue layer, no second language. For a small team, this is existential -- we cannot afford to maintain two codebases.
Compiled delivery without source exposure. Julia is traditionally distributed as source, which is a problem when your code contains trade secrets. We have developed a compilation pipeline that delivers Piccolissimo as prebuilt sysimages -- protecting proprietary code while giving customers instant startup and zero compilation wait. We will discuss the challenges and tradeoffs of shipping Julia as a compiled product.
HPC and GPU deployment. Piccolissimo runs on HPC resources including GPU clusters, leveraging Julia's native GPU and parallelism ecosystem. The same single-language advantage applies here: the control code, the numerics, and the GPU kernels are all Julia -- no CUDA C++ side-channel to maintain.
AI agents as force multipliers. A single-language codebase turns out to be a gift for AI coding agents. An LLM can read the entire stack without context-switching between languages. We ship structured context files and have built reusable agent skills for common workflows -- problem setup, physics references, testing, demo generation. These agents now accelerate both internal development (new features, performance tuning) and user workflows (going from a gate specification to an optimized pulse conversationally). For a small team competing with well-funded incumbents, this is a genuine competitive advantage.
Open core, open science. Piccolo.jl remains fully open source and has been used in peer-reviewed research on robust quantum control (Kamen et al., arXiv:2602.10349) and experimental demonstrations of universal dynamics on Rydberg-atom arrays (Hu et al., arXiv:2508.19075). Piccolissimo extends this with production-grade integrators and compiled delivery, but the science stays open.
In this talk, we share what we have learned building a quantum startup on Julia -- the wins, the workarounds, and why we would do it again.
Currently co-founder and CEO at Harmoniqs, a startup building Julia-based quantum optimal control and calibration software. Previously, a research associate in the Robotics Institute at Carnegie Mellon University. An avid reader, climber, runner, and Bialetti coffee drinker.