SimQuantum: Agentic AI for Quantum Chips

Right now, somewhere in a quantum devices lab, a PhD student is spending hours cooling down a quantum chip, then carefully adjusting voltage knobs until exactly one electron sits in each tiny semiconductor pocket called a quantum dot. Miss the mark even slightly, and the whole setup fails. This painstaking process, known as device tuning, is one of the biggest hurdles to scaling quantum computers. SimQuantum fixes that. It explores a 2D voltage space using a 6-stage decision loop modeled as a POMDP, the same framework used in robotics when systems have to act without perfect information. At each step, it measures voltage, checks if the reading makes physical sense (Data Quality Control), classifies what it sees with a 5-model CNN ensemble trained on 51,000 simulated charge stability diagrams, updates a particle filter belief over possible charge states, and picks the next voltage move using Bayesian optimisation. A safety critic enforces strict voltage limits, and every choice is logged for audit purposes. On top of that, an LLM called Dr. Q (Qwen3-8B) serves as a live copilot in the interface. It sees the full agent state at all times: current stage, voltage position, CNN confidence, belief probability of the target state, and anomaly flags. Users can ask it anything in plain language, and it explains the agent’s actions, flags issues, and produces a summary at the end. The full stack was built on AMD MI300X via ROCm, with Qwen2.5-1.5B served via vLLM as the original inference target. The Streamlit demo runs on HuggingFace Spaces with Dr. Q connected via Fireworks AI (Qwen3-8B) as a portable fallback, pointing the sidebar URL back at an MI300X endpoint switches the backend instantly seamlessly. The agent is able to scan a large 2‑D voltage space (16 V²), automatically detect where a charge transition happens, and correctly label the charge state, all on it's own. Next goal, Navigation: getting the agent to move precisely to the target point on purpose.