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UID:dac_DAC 2026_sess315_RESEARCH279@linklings.com
SUMMARY:SiTA: Exploiting Sparsity in Tensor-Product for Accelerating Quant
 um Readout Error Mitigation
DESCRIPTION:Hanyu Zhang (Zhejiang University); Zhiwei Ye (China Mobile (Su
 zhou) Software Technology); Liqiang Lu, Kaiwen Zhou, Fangxu Guo, and Siwei
  Tan (Zhejiang University); Fangxin Liu (Shanghai Jiao Tong University); S
 ize Zheng (Tsinghua University); and Jianwei Yin (Zhejiang University)\n\n
 Though quantum computing theoretically provides exponential computational 
 advantage, an end-to-end quantum speedup should consider the encoding, com
 pilation, and many other peripheral process that rely on classical compute
 r. Among them, the readout error mitigation turns out to be the bottleneck
 , requiring mitigation of noise errors to achieve high fidelity. To this e
 nd, leveraging the sparsity in the tensor-product has emerged as an appeal
 ing approach to improve computational efficiency. However, existing approa
 ches, based on intermediate data pruning or threshold pruning, suffer from
  the limited accelerator and fidelity loss. In this paper, we propose SiTA
  a quantum error mitigation accelerator that exploits the inherent sparsit
 y of Hilbert space. The key insight lies in that the superposition states 
 generated by quantum algorithms cover only a subset of the Hilbert space (
 output sparsity), and the basis state naturally exhibits bit-level sparsit
 y (input sparsity). Therefore, we introduce a sparse dataflow that feature
 s probability-level and state-level parallelism, which effectively skips c
 alculations related to zero values. Finally, we design its hardware archit
 ecture that supports various computational paradigms of tensor-product, en
 abling acceleration for readout error mitigation. Experiments show that Si
 TA achieves an end-to-end speedup of 4.9X to 1605X over prior mitigation m
 ethods, without sacrificing fidelity.\n\nTopics: Quantum\n\nTrack: DES6. Q
 uantum Computing, Systems\n\nSession Chairs: Hokeun Kim (Arizona State Uni
 versity); BaekGyu Kim (Daegu Gyeongbuk Institute of Science and Technology
 ); Xiang Chen (Peking University); Sangyeob Kim (Yonsei University); Rober
 t M. Radway (University of Pennsylvania); Prashanth Mohan (University of P
 ittsburgh); Xiaoxuan Yang (University of Virginia, Stanford University); R
 amtin Zand (University of South Carolina); Hadi Kamali (University of Cent
 ral Florida); Jiafeng Xie (Villanova University); Prabuddha Chakraborty (U
 niversity of Maine); Junichiro Kadomoto (University of Tokyo); and Yosuke 
 Ueno (RIKEN)\n\n
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