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

arXiv (Cornell University)

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.

Authors 5

1. J. Slim

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   Deutsches Elektronen-Synchrotron , Hamburg 22607 , Germany

2. Saverio Monaco Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron , Hamburg 22607 , Germany

   RWTH Aachen University , Aachen 52062 , Germany

3. Florian Rehm

   European Organization for Nuclear Research

   Affiliation as printed

   European Organization for Nuclear Research (CERN) , Geneva 1211 , Switzerland

4. D. Kruecker
5. Kerstin Borras Aachen

   Deutsches Elektronen-Synchrotron DESY · RWTH Aachen University

   Affiliation as printed

   Deutsches Elektronen-Synchrotron , Hamburg 22607 , Germany

   RWTH Aachen University , Aachen 52062 , Germany