# Leveraging Quantum Measurement Randomness for Shot-Efficient Diffusion Models

**Authors:** Tianhao Guo, Dianxin Zheng, Yuhua Qian, Yaohui Zheng, Yuzhi Yang, Merouane Debbah, Chau Yuen  
**Published:** 30 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.41721) · [PDF](https://arxiv.org/pdf/2610.41721) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.41721.html)

## Key numbers

- **5300×**: …generation quality with $303$ shots per sample against a high-shot reference of 1.6×10^6, about 5300× fewer.

## Abstract

In quantum computing, the readout always carries finite-shot sampling noise. Suppressing this noise through repeated measurements raises the cost of a quantum neural network (QNN). Meanwhile, a stochastic diffusion sampler deliberately adds randomness at each reverse (generation) step. This paper turns the measurement noise from a liability into a resource. Specifically, we use the noise quantum measurement produces anyway to replace part of the noise the sampler would otherwise inject. We characterize the measurement error as multinomial sampling noise propagated through a trainable linear readout. To connect the circuit's Born probabilities to the denoising task, we further formulate the diffusion process on a bounded zero-sum hyperplane mapped to the probability simplex. Therefore, we can employ a compensation noise to match the mean and covariance of the shot-noise-free linear update under some minimum shot conditions. Solving this condition for the shot count gives a state-dependent adaptive budget. For the denoising diffusion implicit model (DDIM), this budget increases as the sampler becomes more deterministic. On an eight-dimensional Gaussian mixture, the schedule achieves comparable generation quality with $303$ shots per sample against a high-shot reference of 1.6×10^6, about 5300× fewer. Latent diffusion experiments on MNIST further validate the proposed mechanism for generation in a learned latent space.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.