Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

Abstract:Direct-register quantum generative models for calorimeter shower simulation tie the quantum output dimension to the image dimension, so the required register size grows with the full image. Recent quantum-assisted methods reduce this pressure only by moving part of the generative task into hybrid latent-variable models. Consequently, current quantum demonstrations remain far below detector-scale geometries used in high-energy physics. We introduce the Quantum Feature Amplification Network (QFAN), which removes this register-size bottleneck by generating an image as a sequence of blocks. Each block is produced by the same small parameterized quantum circuit, conditioned on a compressed summary of the pixels already generated. Reusing the circuit fixes the qubit requirement by block size rather than full image size, while the per-step quantum processing cost is independent of image size for the Pauli-observable family used here. We derive a conservative worst-case bound on shot-noise propagation through the generation chain and give an empirical decoder-capacity heuristic for the reachable sequential depth. A three-qubit circuit with twelve shared variational parameters, closed-form ridge decoders, and a post-hoc residual sampler reproduces per-pixel intensity distributions, inter-pixel correlations, and total energy distributions of calorimeter showers on both simulator and IBM quantum hardware. At this scale, the hardware-simulator gap is consistent with optimization-budget limits dominating over device noise, although the experiments do not causally separate these effects. The results establish a hardware-compatible proof of principle and motivate, but do not validate, larger-scale extrapolations within this circuit family.

Quantum Physics (quant-ph); Computational Physics (physics.comp-ph)

arXiv:2605.16044 [quant-ph]

(or arXiv:2605.16044v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2605.16044 Focus to learn more arXiv-issued DOI via DataCite

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