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

TL;DR

The paper introduces QFAN, a quantum generative model that efficiently produces calorimeter shower images by generating them in blocks with fixed qubit requirements, enabling scalable high-energy physics simulations.

Contribution

QFAN removes the register-size bottleneck in quantum image generation by using a block-based approach with a fixed small quantum circuit, improving scalability for detector-scale geometries.

Findings

Reproduces calorimeter shower distributions on quantum hardware.

Fixed qubit requirement independent of image size.

Empirical results show feasibility of hardware-compatible quantum generation.

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.