Preprint

PUBDB-2026-01455

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Quantum Feature Amplification Network (QFAN) as An Autoregressive Quantum Generative Model

Slim, J. (Corresponding author)DESY* ; Monaco, S.Extern*DESY* ; Rehm, F. ; Kruecker, D.Extern*DESY* ; Borras, K.DESY*

2026

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Please use a persistent id in citations: doi:10.3204/PUBDB-2026-01455

Report No.: arXiv:2605.16044

Abstract: Gate-based quantum generative models have been limited to small images, because their quantum resources grow with the size of the data. The Quantum Feature Amplification Network (QFAN) removes this limit. It reuses a single few-qubit circuit to generate the data block by block, each block conditioned on a compact summary of those before it, with the circuit's measurement outcomes as the source of randomness. The number of qubits is set by the block rather than by the data, and the number of circuits per training step does not grow with the data either. In simulations with only four qubits, QFAN generates full-resolution calorimeter showers from the CaloChallenge Dataset~1 benchmark, 368 voxels each, and learns their correlations and shower-to-shower fluctuations. A three-qubit version generates 12- and 25-pixel images on IBM quantum hardware. This brings high-dimensional scientific data within reach of near-term quantum generative models.

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Contributing Institute(s):

1. LHC/CMS Experiment (CMS)

Research Program(s):

1. 611 - Fundamental Particles and Forces (POF4-611) (POF4-611)
2. ENGAGE - Enabling the Next-Generation of Computational Physicists and Engineers (101034267) (101034267)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2026

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