DyREM: Dynamically Mitigating Quantum Readout Error with Embedded Accelerator

Quantum readout error is the most significant source of error, substantially reducing the measurement fidelity. Tensor-product-based readout error mitigation has been proposed to address this issue by approximating the mitigation matrix. However, this method inevitably encounters the dynamic generation of the mitigation matrix, leading to long latency. In this paper, we propose DyREM, a software-hardware codesign approach that mitigates readout errors with an embedded accelerator. The main innovation lies in leveraging the inherent sparsity in the nonzero probability distribution of quantum states and calculating the tensor product on an embedded accelerator. Specifically, using the output sparsity, our dataflow dynamically downsamples the original mitigation matrix, which dramatically reduces the memory requirement. Then, we design DyREM architecture that can flexibly gate the redundant computation of nonzero quantum states. Experiments demonstrate that DyREM achieves an average speedup of $9.6 \times \sim 2000 \times$ and fidelity improvements of $1.03 \times \sim 1.15 \times$ compared to state-of-the-art readout error mitigation methods.