Quantum Dot Live Twin

Quantum dot labs read device states via dispersive or RF measurements, operating much faster than the classification loops meant to track them. When a twin lags behind, drift can go unnoticed until valuable fridge time is lost recharacterising the device. We built a live digital twin designed to stay in sync with a quantum dot’s changing state under exactly that kind of pressure. It's a monitoring and synchronisation layer a lab can trust between tuning runs, not a system that wrestles the operator for gate control. The frames come from a real QArray-simulated device trajectory, powered by Rust for speed. In a 2000-frame run on an AMD MI300X (PyTorch/ROCm), the serial CPU baseline hits a worst-case lag of 0.155s. GPU batching without load-shedding actually performs worse under pressure, spiking to 3.304s as backlog builds toward the end. A rule-based triage agent solves this, holding worst-case lag to just 0.021s while cutting compute further, routing 235 full passes, 15 light passes, and 44 skips in real time based on queue depth, staleness, and drift signals, a decision actively used by the control loop. An LLM supervisor (Fireworks AI) sits above the triage agent, monitoring backlog trends in the background to retune thresholds without slowing the real-time path. The adaptive, LLM-tuned threshold architecture is running end-to-end. Next up is longer soak testing to capture threshold updates under sustained drift, which is a clear path to differentiation as the system scales to real hardware. The same stream is exposed as a true QCoDeS Instrument, so a lab can connect this twin directly to their instruments with no extra integration layer. That’s the difference between a demo and something a lab could plug in this week. Results arrive in an interactive control console built for the bench, not a slide deck. Future work includes moving from synchronous micro-batching to a fully asynchronous pipeline and training the classifier weights.