SQGen：基于潜变量调制量化张量列车的结构化量子图像生成
SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains
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中文总结 AI 辅助
研究旨在解决NISQ硬件上直接从量子系统生成图像的问题，核心方法是提出基于潜变量调制量化张量列车的SQGen，主要贡献为能稳定训练，端到端生成图像且无需经典解码器，在真实量子硬件上有可行性。
中文摘要 AI 辅助
直接从量子系统生成图像是NISQ硬件上一个有吸引力但尚未解决的目标。现有量子发生器面临诸多障碍，如阻碍可训练性的贫瘠高原、昂贵的量子电路制备以及随深度侵蚀量子信息的硬件噪声。我们提出了SQGen，一种基于具有潜变量调制架构的量化张量列车的全量子发生器。具体而言，它将目标像素分布的QTT键索引提升为辅助键量子比特，还引入潜变量调制。训练时在经典系统中创建可微模型，训练后每个算子一对一映射到原生量子门。实验表明SQGen能稳定训练，可从无经典解码器的浅电路端到端生成图像，在真实量子硬件上有可行性。
英文摘要
Generating images directly from quantum systems is an attractive but unresolved goal on NISQ hardware. Existing quantum generators face several coupled obstacles: barren plateaus that block trainability, expensive quantum circuit preparation, and hardware noise that erodes quantum information with depth. A further difficulty is producing image-scale output without a classical decoder, whose use would otherwise break the end-to-end quantum advantage. We propose SQGen, a full quantum generator built on a quantized tensor train (QTT) with a latent modulation architecture. Specifically, SQGen promotes the QTT bond index of the target pixel distribution to ancilla bond qubits, so that each circuit site operates locally on a bond register plus the two physical qubits that carry the row- and column-bit of one image scale. We further introduce latent modulation: each re-uploading rotation is factorized at the angle level into a trainable main path plus an additive latent term, reducing to the trainable main path when the latent term is disabled. During training, we create a differentiable model in the classical system under gate-compatibility constraints, with a torus prior as the latent distribution. After training, every operator maps one-to-one to a native quantum gate, yielding a compact, deployable quantum circuit with no classical decoder in the inference path. Together, these design choices address the obstacles raised above. Extensive experiments on image datasets and synthetic data demonstrate that SQGen trains stably, generates images end-to-end from a shallow circuit with no classical decoder, and shows promising feasibility on real quantum hardware.
