Liu, Y., Chong, Y. S., Lienhard, B., Fan, M., Goh, W. L., Nambiar, V. P., & Do, A. T. (2025). Qubit-State Discrimination using Neural Networks with Rapid and Energy-Efficient Compute Arrays. In (Editor), 2025 IEEE International Symposium on Circuits and Systems (ISCAS). https://doi.org/10.1109/iscas56072.2025.11043488
Abstract:
Neural networks (NNs) implemented on field-programmable gate arrays (FPGAs) provide fast, high-fidelity solutions for processing readout signals from quantum information processors. However, application-specific integrated circuits (ASICs) instead of FPGAs hold the potential for improved performance, a largely unexplored path. This work proposes specialized hardware for NN-based qubit-state discrimination. We optimize the NN architecture to minimize resource requirements by reducing the layer width, employing linear activation functions, and weight quantization. Quantization-aware training is used to preserve accuracy despite these optimizations. Next, a compute array employing output stationary dataflow is chosen to process the NN workload. The compute array with abundant multipliers and adders can complete one NN inference in 63 ns, which makes it a good candidate for real-time qubit-state discrimination.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - SPF
Grant Reference no. : C210917009
This research / project is supported by the National Research Foundation Singapore - Quantum Engineering Programme 2.0
Grant Reference no. : NR2021-QEP2-03-P07