This repository contains the full dataset and analysis code accompanying the manuscript“Emergent AdS₂ Geometry from Quantum Codes: Experimental Evidence for the Fion Substrate Framework.” Using a 5-qubit ] stabilizer code implemented on IBM Quantum hardware, we reconstruct an effective information-geometric metric from 12,000 measurement shots. Bias-corrected mutual information, hyperbolic and Euclidean embeddings, stress minimization and Akaike Information Criterion (AIC) model comparison all point to a hyperbolic AdS₂ geometry with curvatureκ = −1.0012 ± 0.0039 (90% BCa CI), statistically equivalent to the theoretical value κ = −1 (TOST equivalence test, ΔAIC = 30.32 in favour of the AdS₂ model). In the associated manuscript we interpret these results within the “fion substrate framework”, a unified informational model in which elementary quantum codes generate spacetime, matter and (at high coherence) mind-like dynamics. The Figshare materials, however, focus on the quantum-information and geometric side of the work. Contents of the repository raw IBM Quantum measurement data (counts) for the 5-qubit code (12,000 shots); processed mutual-information matrices and distance matrices; Python scripts for metric embedding, bootstrap uncertainty estimation and equivalence testing (BCa + TOST); bootstrap distributions of the curvature κ and model-comparison summaries (AIC, stress); documentation on how to reproduce all numerical results and figures reported in the paper. The dataset is intended to allow full reproduction and independent re-analysis of the emergent-geometry reconstruction, and to provide a starting point for extending the protocol to larger quantum codes and higher-dimensional AdS geometries
This repository contains the full dataset and analysis code accompanying the manuscript“Emergent AdS₂ Geometry from Quantum Codes: Experimental Evidence for the Fion Substrate Framework.” Using a 5-qubit ] stabilizer code implemented on IBM Quantum hardware, we reconstruct an effective information-geometric metric from 12,000 measurement shots. Bias-corrected mutual information, hyperbolic and Euclidean embeddings, stress minimization and Akaike Information Criterion (AIC) model comparison all point to a hyperbolic AdS₂ geometry with curvatureκ = −1.0012 ± 0.0039 (90% BCa CI), statistically equivalent to the theoretical value κ = −1 (TOST equivalence test, ΔAIC = 30.32 in favour of the AdS₂ model). In the associated manuscript we interpret these results within the “fion substrate framework”, a unified informational model in which elementary quantum codes generate spacetime, matter and (at high coherence) mind-like dynamics. The Figshare materials, however, focus on the quantum-information and geometric side of the work. Contents of the repository raw IBM Quantum measurement data (counts) for the 5-qubit code (12,000 shots); processed mutual-information matrices and distance matrices; Python scripts for metric embedding, bootstrap uncertainty estimation and equivalence testing (BCa + TOST); bootstrap distributions of the curvature κ and model-comparison summaries (AIC, stress); documentation on how to reproduce all numerical results and figures reported in the paper. The dataset is intended to allow full reproduction and independent re-analysis of the emergent-geometry reconstruction, and to provide a starting point for extending the protocol to larger quantum codes and higher-dimensional AdS geometries
This report offers a comprehensive academic synthesis of the information-theoretic paradigm in fundamental physics, in which spacetime geometry, vacuum structure, and gravitational dynamics arise from the entanglement and error-correcting properties of quantum many-body systems. Beginning with the Toric Code, Color Codes, and string-net condensates, the review explains how gauge fields, fermionic statistics, and virtual particle structure emerge from stabilizer constraints and topological order. It then explores Fracton phases and subsystem symmetries as geometric substrates capable of reproducing the constraints and potentials of linearized gravity. The analysis extends into holographic quantum error correction—including MERA, the HaPPY code, and random tensor networks—to show how bulk locality, black hole entropy, and AdS geometry emerge from entanglement structure. The Hyperbolic Fracton Model is examined as a synthesis that unifies fractonic mobility constraints with holographic scaling and area-law entropy. The report supports a unified “code-theoretic” framework in which physical laws and spacetime arise from the structure of an underlying quantum code.
This paper presents the SYK TENSOR Quantum Information Network (QIN) as the firstcomplete, discrete, and empirically validated pre geometric analysis of spacetime emergencein the history of physics a true Theory of Everything. For the first time, spacetime geometryis rigorously derived from a finite capacity substrate of maximally chaotic Majorana qubitsgoverned by random all to all SYK interactions, stabilized by holographic tensor networkquantum error correcting codes, and softly regulated with Gaussian form factors to achievebounded UV entanglement variance (stability coefficient γ = 1.0–1.2). Large scale numericalexperiments (N = 8–64 qubits, code distances d = 3–15, Hilbert dimension up to 232) andexact toy models validate five sharp, falsifiable predictions with unprecedented precision: (1)maximal chaos scrambling (λL = 0.85±0.12), (2) perfect holographic bulk reconstruction viathe Ryu-Takayanagi formula (ρ = 1.00±0.12, p = 7.67×10−144), with exponential hyperbolicvolume growth confirming negative curvature (κ = 1.05 ± 0.08), (3) resolution of the blackhole information paradox through traversable wormhole teleportation (fidelity advantage8.2× classical baseline, p ≪ 10−12), (4) emergent gravity scaling (r = −0.94 between λL andGeff = 1/λL), and(5)Standard Model force unification via flavored holographic gauging withexact charge conservation. Mathematical consistency is established through exact OTOCderivation in Jackiw-Teitelboim gravity duality, bounded regulated entanglement entropyvariance (γ2 ≈ 2|b|), and rigorous pre geometric origin of AdS/CFT holography. Thisframework resolves UV divergences, the information paradox, emergent geometry, and forceunification without continuum assumptions, extra dimensions, or supersymmetry, providing,for the first time in history, a fully discrete, finite, and computationally accessible origin ofspacetime itself. All results are reproducible via provided Google Colab notebooks, withdirect pathways for near term quantum hardware verification (N ¿ 100 logical qubits).
Replication Data and Code for: "Capturing the Universality Class of the 2D Ising Model via Symmetry-Corrected Machine Learning and Finite-Size Scaling" This repository contains the complete source code, simulation datasets, and analysis scripts required to reproduce all findings presented in the associated manuscript. The study introduces a symmetry-corrected machine learning pipeline (Gauge-Fixed PCA + Logistic Regression) to identify phase transitions and estimate critical temperatures with high precision, validated on the two-dimensional Ising model. Repository Contents: 01_generate_data.py — Generates 2D Ising model spin configurations using the Wolff cluster algorithm. Produces equilibrium datasets for lattice sizes L∈{32,48,64,96}L \in \{32, 48, 64, 96\} L∈{32,48,64,96} with 10,000 independent configurations per temperature point. 02_analysis_pipeline_v2.py — Core analysis script: performs gauge fixing (σ′=σ⋅sgn(m)\sigma' = \sigma \cdot \mathrm{sgn}(m) σ′=σ⋅sgn(m)), PCA dimensionality reduction (d=16d = 16 d=16, cross-validated), confusion-scheme classification with parabolic sub-grid interpolation, bootstrap uncertainty quantification (Nboot=50N_{\rm boot} = 50 Nboot=50), and pFMp_{\rm FM} pFM computation with isotonic calibration. Outputs summary_ml_v2.json. 03_plot_main_figures.py — Reproduces Figures 1–6 of the main manuscript. 04_plot_supplementary_v2.py — Reproduces Supplementary Figures S1–S5 and Table 1. ablation_preprocessing_S5.py — Standalone preprocessing ablation script: runs raw and ∣m∣|m| ∣m∣-only comparisons and generates Supplementary Figure S5. Does not modify main results. summary_ml_v2.json — Pre-computed results (gauge-fixed pipeline) for immediate reproduction of all main figures without re-running Monte Carlo simulations. summary_ml_v2_raw.json — Pre-computed ablation results: raw (no gauge fixing) preprocessing, L∈{32,64,96}L \in \{32, 64, 96\} L∈{32,64,96}. summary_ml_v2_absm.json — Pre-computed ablation results: ∣m∣|m| ∣m∣-only preprocessing, L∈{32,64,96}L \in \{32, 64, 96\} L∈{32,64,96}. Table1_Tc_Comparison.tex — LaTeX source for Table 1. requirements.txt — Python dependencies. README.txt — Step-by-step reproduction instructions. Methodology Highlights: System sizes: L∈{32,48,64,96}L \in \{32, 48, 64, 96\} L∈{32,48,64,96} 10,000 independent configurations per temperature point; Wolff cluster algorithm with 10410^4 104 thermalization steps Symmetry correction: global Z2\mathbb{Z}_2 Z2 gauge fixing prior to PCA Confusion scheme: validation cross-entropy minimization over T0∈[2.10,2.45]T_0 \in [2.10, 2.45] T0∈[2.10,2.45] (60 candidate values, band Δ=0.05\Delta = 0.05 Δ=0.05, 5-fold CV) Bootstrap: 50 configuration-level resamples; parabolic sub-grid interpolation for discretization bias reduction Key result: Tc≈2.2717(8)T_c \approx 2.2717(8) Tc≈2.2717(8) for L=96L = 96 L=96 (0.6%0.6\% 0.6% from Onsager); universality class confirmed via FSS data collapse with ν=1\nu = 1 ν=1
This record provides the complete figure archive and figure-generation code for the manuscript: “A Differential Null Test for Informational Corrections to Gravity-Related State Reduction.” The archive contains the final manuscript and Supplemental Material figures in both PDF and PNG formats, together with a reproducible Python/Matplotlib script for regenerating the figure set. The figures support a theoretical proposal for a differential null test of informational corrections to gravity-related quantum state reduction. The manuscript develops a matched-pair visibility-residual framework in which ordinary gravitational and environmental baseline terms are cancelled, leaving a testable bound on the informational coupling parameter. The included figures are: Fig1 — main manuscript figure showing the differential null-test structure and visibility-residual logic. FigS1–FigS5 — supplementary figures supporting the branch-density construction, perturbative regime, visibility residuals, coupling bounds, and source-to-energy schematic. The code package includes: generate_prl_figures.py — Python script for regenerating the full figure set. requirements.txt — minimal package requirements. PDF and PNG output folders. SHA256 manifest for file-integrity checking. This Zenodo record is intended to support transparency, reproducibility, and independent inspection of the visual materials associated with the manuscript. The figures are synthetic/theoretical benchmark illustrations and do not represent observational detections or experimental data. Suggested citation use: cite this record when referring to the reproducible figure-generation code or archived figure files associated with the manuscript.
This upload contains the manuscript, LaTeX source, and Python code for the paper: "Non-Orientable Topology in a Stabilizer Circuit: Experimental Demonstration of a Klein Bottle Quantum Error-Correcting Code" We report the first experimental implementation of a Klein bottle stabilizer code on superconducting quantum hardware. The code achieves a 138× enhancement of the predicted antipodal syndrome signature (44.2% frequency, Z=499σ) on IBM Fez, confirmed on three independent processors (IBM Fez, Marrakesh, Torino) and in four simultaneous parallel code instances. The code uses 25% fewer physical qubits than the square toric code at equivalent code distance. The Python code implements the Klein bottle stabilizer code on a 4×2 lattice, reproduces the antipodal signature from the initial 26.3% to the optimised 44.2%, and includes hardware verification across three processors and MWPM logical error rate simulation. All hardware job IDs are provided in the README for independent reproducibility. Contents: paper.pdf — manuscript latex/ — LaTeX source and figures code/ — Python scripts (circuit builder, verification, simulation) README.md — instructions and complete hardware job ID table All hardware experiments were performed on IBM Fez (Heron r2, 156 qubits) via IBM Quantum Runtime. The code is released under MIT license; the manuscript under CC BY 4.0.
This data repository contains the information necessary to reproduce the main results of the paper “Quantum computational displacement sensing”. The repository contains both the data required to generate the figures of the main manuscript and the supplemental material, as well as source code for running the experiments and simulations presented in the paper. There are two main folders: code_simulation (which consists of the training code and the baseline simulations) and code_experiment (which covers the experimental code). The corresponding requirements for running them are in the zipped folder. The data folder contains all simulation and experimental raw and processed data generated. The figures folder contains all the plots presented in the manuscript.