SQGen: Structured Quantum Image Generation with Latent-Modulated Quantized Tensor Trains

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 generating image-scale outputs without a classical decoder. We propose SQGen, a decoder-free quantum generator whose circuit follows a quantized tensor train (QTT) skeleton with latent modulation. Specifically, SQGen promotes the QTT bond index of the target pixel distribution to ancilla bond qubits, so that each circuit site acts on a bond register, the two physical qubits that carry the row- and column-bit of one image scale, and a coarse-to-fine link to the previous scale. The latent enters through latent modulation: an exact angle-level factorization of each re-uploading rotation into a trainable main path plus an additive latent term, reducing to the 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. Every trained operator maps one-to-one to a native quantum gate, yielding a compact, deployable quantum circuit with no classical decoder in the inference path. 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.