RobustState: Variational Quantum State Preparation with Noise-Aware Gradients

July 2022 – Aug. 2022, MIT
Hanrui Wang* ,Yilian Liu*, Pengyu Liu*, Jiaqi Gu, Zirui Li, Zhiding Liang, Jinglei Cheng, Yongshan Ding, Xuehai Qian, Yiyu Shi, David Pan, Fred Chong, Song Han
  • ASPLOS2023 in submission.
  • Proposed RobustState, a state preparation framework that can dynamically adapt to the noise pattern of specific quantum computers to improve the robustness of prepared states.
  • Proposed to compute the loss between tomography states on the real machine and ideal states, and then back-propagate the noise-impacted gradients through a classical simulator such that the trained parameters will be resilient to real quantum noise.
  • Conducted experiments on real IBM superconducting quantum machines and demonstrated 7× coherent error reduction and n× fidelity improvement for 5-qubit states over the arithmetic-decomposition baseline