Realizing adaptive algorithms on FPGA for spin-qubit readout (part 1)

Developing adaptive on-chip algorithms requires realistic modeling of spin-qubit readout dynamics and robust machine learning (ML) approaches for fast state discrimination. In this work, we present a realistic simulation framework that captures the key physics of semiconductor spin qubits, including sensor dynamics, circuit response, and stochastic noise processes [1]. Leveraging this framework, we design and benchmark ML-based classifiers for single- and multi-qubit readout, demonstrating that neural networks can outperform conventional thresholding methods in real time [2]. The resulting models are optimized and developed to be deployed on FPGA hardware, forming the algorithmic foundation for the real-time implementations discussed in part 2. Together, these developments mark a significant step toward scalable, ML-enhanced feedback and adaptive control in semiconductor spin-qubit systems.