Data of the:Geometric Origin of Dark Matter: Bridging Quantum Simulation with Cosmology via Noise-Induced Z4 Locking on a Holographic Processor

### DATASET SUMMARYThis dataset provides the raw experimental results and software analysis framework for the paper: "Geometric Origin of Dark Matter: Bridging Quantum Simulation with Cosmology via Noise-Induced Z4 Locking on a Holographic Processor". The provided data supports the observation of a Z4 symmetry-protected topological state emerging at a critical cooling factor of gamma_c = 0.25, simulated on a 133-qubit superconducting quantum processor. ### HARDWARE SPECIFICATIONS- Backend: IBM Quantum "ibm_torino" (Heron r1 architecture)- Qubit Mapping: Snake-like chain configuration (L=16 to L=28)- Calibration Date: 2025-12-31 ### DATA CONTENT (Directory Structure)1. /Data: - raw_counts_sniper_scan_d59q2qjht.json: Bitstring counts for Fig S2/S3 Sniper Scans. - raw_counts_fss_L16_to_L28_d59q7e1sm.json: Scaled system data for Fig S4 Finite-Size Scaling.2. /Scripts: - fss_data_collapse_analysis.py: Python routine to extract critical exponents (nu=1, beta=0.125). - zne_mitigation_richardson.py: Zero-Noise Extrapolation (ZNE) post-processing code.3. /Circuits: - holographic_sedimentation_protocol.qasm: OpenQASM 3.0 circuit definitions. ### USAGE & REPRODUCIBILITYThis dataset allows for the independent verification of the 0.018 renormalization gap reported in the main text. By running the provided scripts, researchers can reproduce the universal data collapse that identifies the system within the 2D Ising Universality Class. Print Archive :https://zenodo.org/records/18108172 ### 许可证本数据集根据 Apache License 2.0 发布。