ML-Powered FPGA-based Real-Time Quantum State Discrimination Enabling Mid-circuit Measurements

Understand

Accurate and timely quantum state discrimination is critical for quantum computing, particularly in protocols requiring mid-circuit measurement (MCM) and conditional feed-forward.

• While classical state readout in transistors is near-instantaneous, identifying the state of superconducting qubits remains latency-limited and error-prone.
• Existing approaches rely on post-processing measurement data transferred to host computers, introducing delays on the order of milliseconds -- vastly exceeding the coherence time of quantum states, which typically last only hundreds of microseconds.
• To bridge this latency gap, we present an in-situ machine learning (ML) inference engine implemented on a field-programmable gate array (FPGA), enabling real-time quantum state discrimination within 40 ns.