A benchmark dataset for quantum noise effects in amplitude-encoded images

Ola Al-Ta’ani

Published Oct 16, 2026• 0 citations

Abstract

Abstract Quantum image processing methods that rely on amplitude encoding are sensitive to channel-induced noise, yet reusable benchmarks for evaluating these effects under controlled conditions remain limited. We present QN-AEI, a simulation-based benchmark constructed from 200 images in the BSDS500 training subset. Each image is resized to 64 × 64 pixels and normalized to a 4096-dimensional amplitude vector representing a 12-qubit quantum state, then subjected to three noise channels (amplitude damping, Pauli-Z dephasing, and depolarizing noise) at four levels, η ∈ {0.1, 0.3, 0.5, 0.7}, with 200 stochastic trajectory realizations per image–channel–noise condition. This yields 2,400 conditions, 480,000 noisy realizations, and 1,966,080,000 normalized perturbation samples in total. Each record pairs the clean image and amplitude vector with its noisy statevectors, raw and normalized perturbations, and condition-level metadata; the release also includes source-image provenance, a dataset manifest, validation outputs, summary statistics, documentation, and the MATLAB generation and validation code. QN-AEI supports reproducible benchmarking of quantum-noise characterization, denoising, uncertainty and reliability analysis, and bounded-domain perturbation modeling.

DOI: 10.21203/rs.3.rs-11381515/v1

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