AI Qubit Pulse Optimizer
Watch a reinforcement-learning-style optimizer train a qubit control pulse to survive detuning and dephasing noise, live on a 3D Bloch sphere, with a fidelity learning curve and pulse-shape readout.
Real quantum computers can't rely on a perfect, textbook control pulse — detuning drifts and decoherence corrupt every gate, and this is exactly the gap AI-driven calibration is starting to fill. This simulator shows a segmented control pulse trying to flip a qubit from |0⟩ to |1⟩ on a live 3D Bloch sphere while random detuning and dephasing noise fight it every cycle. A stochastic hill-climbing optimizer — the same family of zeroth-order methods used for real pulse-level quantum control — perturbs the pulse shape episode by episode, keeping any change that raises fidelity, so you watch the pulse bars reshape themselves and the fidelity learning curve climb in real time as the AI learns to out-run the noise.
Watch a reinforcement-learning-style optimizer train a segmented qubit control pulse to survive random detuning and dephasing noise, live on a 3D Bloch sphere with a fidelity learning curve.
3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install