FPGA-based Machine Learning for In-situ Qubit State Discrimination on QubiC

The integration of machine learning based field-programmable gate array (FPGA) control systems holds tremendous potential for advancing quantum computing by enabling real-time analysis and optimization of qubit readout, resulting in enhanced performance and efficiency. Leveraging the capabilities of the QubiC (Qubit Control) system, we deploy a real-time qubit state discrimination machine learning model on an FPGA. This model not only swiftly identifies the current state of the qubit but also delivers an estimation of the readout classification accuracy. This FPGA-based machine learning controller is constructed through a process involving machine learning model training, pruning and quantization, followed by compilation and assembly on an FPGA. This approach leads to minimal latency and exceptional efficiency in discriminating qubit states, paving the way for substantial advancements in the field of quantum computing.