ARTERY: Fast Quantum Feedback using Branch Prediction

Quantum feedback makes the execution of dynamic quantum circuits possible and is widely used in quantum algorithms. However, due to the inherent computation and transmission cost, the latency of the quantum feedback becomes a considerable burden on the current quantum algorithm. The dynamic property of the feedback also makes the gates blocked until the feedback is finished. In this paper, we propose ARTERY, which uses branch prediction to support instruction pre-execution and speed up the feedback. ARTERY integrates historical statistics of branches and a real-time readout pulse analysis to predict the branch. With this idea, we build up a reconciled branch predictor that concatenates the historical statistics of branches and a real-time branch circuit speculation obtained from the readout-pulse trajectory predictor. We further explore the implementation of peripheral hardware for feedback, including a scalable inter-FPGA connection via the backplane, a feedback trigger mechanism for dynamic instruction timing, and an adaptive pulse sampling technique to maximize the hardware bandwidth. ARTERY accelerates quantum feedback process by 2.07× compared to the state-of-the-art method, with over 90% prediction accuracy, achieving 1.24× fidelity improvement.