QubiCML: ML-Powered Real-Time Quantum State Discrimination Enabling Mid-Circuit Measurements

This work addresses the critical challenge of accurately and swiftly identifying quantum states in superconducting quantum processors, with a particular focus on qubit (two-level) states. It introduces QubiCML, an innovative FPGA-based system designed for in-situ, real-time quantum state discrim-ination, which is essential for mid-circuit measurements and the implementation of advanced error correction techniques. By integrating a multi-layer neural network on the FPGA platform, QubiCML achieves exceptional accuracy and low latency in distinguishing quantum states. Traditional methods of quantum state discrimination rely heavily on offline data processing, which is inadequate due to the brief coherence time of quantum states and significant communication delays. QubiCML overcomes these limitations by utilizing in-situ machine learning for state discrimination, enabling real-time state determination in just 54 ns. It optimizes readout RF pulses and digital local oscillator (DLO) signals, significantly enhancing the fidelity of state discrimination. This capability enables mid-circuit measurements (MCM), which we successfully deployed using the QubiCML system. The system has been rigorously evaluated on in-house superconducting quantum processors, demonstrating an impressive average accuracy of 98.5%. QubiCML's modular architecture supports scalability across various qubits, making it adaptable to different quantum computing setups. Its real-time feedback capabilities are particularly beneficial for efficient quantum algorithm development and optimization. This system represents a groundbreaking advance-ment in the field of quantum computing, offering a robust tool for real-time quantum state discrimination. QubiCML has the potential to become the standard method for state discrimination, providing the quantum computing community with a powerful solution to push the boundaries of current technology.