AI-Driven Device Modeling For Next Generation Quantum Applications

Key takeaways: Accelerate quantum system design by rapidly developing accurate device models for circuit-level simulation. Reduce manual model-tuning effort with Keysight ML Optimizer, enabling efficient, derivative-free extraction of advanced compact model parameters. Extend compact models to cryogenic temperatures using deep learning neural networks (NN) to learn unmodeled residual behavior from 4 K or below measurement data. Quantum computing is advancing rapidly from isolated research device