Experimental data for auxiliary-free replica shadow measurements on a superconducting quantum processor
Year: 2026
Type: dataset
Abstract: This dataset contains experimental data, numerical reference results, readout calibration matrices, and analysis notebooks for the auxiliary-free replica shadow (AFRS) protocol implemented on a superconducting quantum processor. The data are organized into four system–replica configurations: 1+1, 2+2, 3+3, and 4+4, where n+n denotes an n-qubit system together with its n-qubit replica. The 1+1 and 2+2 experiments benchmark global AFRS through measurement probabilities and squared-density-matrix reconstruction. The 3+3 experiment uses local AFRS to reconstruct the reduced second moment of a single-qubit subsystem for different initial-state angles. The 4+4 experiment consists of a four-qubit Su–Schrieffer–Heeger (SSH) chain and its replica. It includes measurements for three coupling configurations, (v,w) = (1,0.5), (1,1), and (1,2). For each configuration, tilted ferromagnetic initial states with θ = π/2, π/3, and π/4 are prepared and subsequently subjected to a quantum quench. The results for θ = π/2 and π/3 are presented in the associated manuscript. Experimental and theoretical results for edge and bulk sites are provided to study second-order Rényi entanglement asymmetry and a finite-size signature of the quantum Mpemba effect. The archive includes experimental acquisition data, processed probabilities, purity and squared-density-matrix estimates, zero-noise-extrapolation results using circuit-folding factors 1 and 3, and entanglement-asymmetry trajectories. Files are provided as Python pickle files, plain-text tables, and Jupyter notebooks. The accompanying README describes the directory structure, naming conventions, data formats, reported software environment, and data-reading examples.
Author: Zhang, Xutao
Institution: Zhejiang University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220900340
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