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About this paper
Chi Zhang, Mengxin Zheng, Qian Lou, Fan Chen
DiffQ treats variational quantum algorithm initialization as a generation problem, using a task description to guide a diffusion model that denoises noise into circuit parameters. Separately trained models improve initial loss and convergence steps on four of five tasks. On held-out 2D TFI instances, average initial loss was −12.18 versus −3.23, with 180.83 versus 235.93 convergence steps; results do not establish cross-task transfer.
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