无需高保真量子随机化，用噪声通道生成量子系综
Mixed-State Quantum Denoising Diffusion Probabilistic Model
- 用退相干噪声通道替代传统随机化单元，简化实现
- 在量子系综生成任务中成功复现目标态分布
- 适合资源受限的近中期量子设备使用
生成式量子机器学习因其能生成期望分布的量子态而备受关注。在各类量子生成模型中，量子去噪扩散概率模型（QuDDPM）通过逐步学习有效解决了训练难题，但其对高保真随机化单元的要求在近中期设备上难以实现。本文提出混合态量子去噪扩散概率模型（MSQuDDPM），消除对随机化单元的需求。方法上，将退极化噪声通道融入前向扩散过程，并在反向去噪步骤中引入参数化量子电路与投影测量。同时，采用余弦指数噪声插值、单比特随机辅助量子比特及基于超保真度的损失函数以提升收敛性。在量子系综生成任务上的实验表明，该模型能有效生成目标量子态分布。
原文摘要 · Abstract (English)
Generative quantum machine learning has gained significant attention for its ability to produce quantum states with desired distributions. Among various quantum generative models, quantum denoising diffusion probabilistic models (QuDDPMs) [Phys. Rev. Lett. 132, 100602 (2024)] provide a promising approach with stepwise learning that resolves the training issues. However, the requirement of high-fidelity scrambling unitaries in QuDDPM poses a challenge in near-term implementation. We propose the \textit{mixed-state quantum denoising diffusion probabilistic model} (MSQuDDPM) to eliminate the need for scrambling unitaries. Our approach focuses on adapting the quantum noise channels to the model architecture, which integrates depolarizing noise channels in the forward diffusion process and parameterized quantum circuits with projective measurements in the backward denoising steps. We also introduce several techniques to improve MSQuDDPM, including a cosine-exponent schedule of noise interpolation, the use of single-qubit random ancilla, and superfidelity-based cost functions to enhance the convergence. We evaluate MSQuDDPM on quantum ensemble generation tasks, demonstrating its successful performance.
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