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A Spatial-Topology Preserving Compiler for Quantum Many-Body Systems Simulation

Xiangyu Ren, Yuexun Huang, Zhaohui Yang, Yuchen Zhu, Tsung‐Wei Huang, Tsung-Yi Ho, Zhiding Liang, Antonio Barbalace

Published Jul 25, 2026• 0 citations
Publisher

Abstract

GadIR artifact evaluation This self-contained directory reproduces Figures 10--15, 17, and 18(d) from the packaged Hamiltonians and generated random instances. It contains one GadIR frontend, four architecture backends, the two analysis workflows, and the baseline code used by the experiments. Full reproduction regenerates the result tables from the raw benchmarks. The bundled Figure 15 table supports plot-only validation and is overwritten by reproduce. Repository: https://github.com/xiyurain/GadIR Archive: https://doi.org/10.5281/zenodo.21550065 Requirements Use a machine with Bash, Git, Conda, a C/C++ toolchain, and stable Rust/Cargo. Intel, AMD, and Apple CPUs are supported. The experiments are CPU-only; at least 16 CPU cores, 32 GB RAM, and 20 GB of free disk are recommended. No GPU or quantum hardware is required. Network access is needed only while installing the environments. Install Miniconda (or Anaconda), Git, and the host compiler first. On Ubuntu/Debian, the host packages can be installed with: sudo apt-get update sudo apt-get install -y build-essential curl git Install Rust with rustup: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh source "$HOME/.cargo/env" conda --version git --version rustc --version cargo --version Installation Clone the public repository (or unpack the Zenodo archive), enter this directory, and run the installation script: git clone git@github.com:xiyurain/GadIR.git cd GadIR/GadIR_AE ./prepare.sh prepare.sh creates three pinned environments under .envs/: analysis: Python 3.10, Qiskit 1.4.3, Qiskit Aer 0.17.1, PyZX 0.9.0, NumPy, pandas, SciPy, matplotlib, Stim/sinter 1.15.0, PyMatching 2.3.0, TQEC, GraphiQ 0.1.1, and Jupyter Notebook; quclear: Python 3.10 and Qiskit 1.1.1. 2qan: Python 3.9 and Qiskit Terra 0.20.2 (Qiskit 0.36.2). The script also builds and installs the packaged Rustiq Python extension into the analysis environment and installs Tweedledum 1.1.1 without build isolation before Qiskit Terra 0.20.2 in the 2QAN environment. TQEC and GraphiQ are installed at the exact revisions recorded in environments/analysis.yml. GraphiQ is retained for compatibility with the submitted installation check and is installed from the bundled wheel built at the recorded revision without its obsolete Qiskit 0.38 dependency; the Figure 15 workflow uses the packaged GSSynth backend. Manual environment activation is unnecessary. An installation can be checked with: ./.envs/analysis/bin/python -c "import qiskit, pyzx, rustiq, stim, sinter, pymatching, tqec, graphiq" ./.envs/quclear/bin/python -c "import qiskit" ./.envs/2qan/bin/python -c "import qiskit" If SSH access to GitHub is unavailable, download the Zenodo archive instead of cloning. The pinned TQEC revision still requires network access during prepare.sh. Running the artifact All commands are run from GadIR_AE/: ./run.sh smoke ./run.sh reproduce ./run.sh plot smoke runs one small input for each architecture backend and reduced-size Figure 17/18(d) analyses. reproduce compiles all selected benchmarks, writes fresh tables under results/, and draws all eight figures. plot only redraws from the tables already generated under results/. To generate all paper figures from completed AE results, use the provided plot-only notebook: ./.envs/analysis/bin/jupyter notebook generate_all_figures.ipynb The notebook draws Figures 10--15, 17, and 18(d) into output/; run ./run.sh reproduce first. The submitted artifact appendix refers to Figure 18(c) in this sentence. This is a labeling typo: the included analysis and notebook reproduce Figure 18(d). The workflows run in this order: superconducting, neutral atom, FTQC, MBQC, and analysis. Full reproduction is intentionally long: FTQC sampling and all 84 Figure 15 compiler jobs can take hours to days. smoke also executes real compiler/simulator paths and can take tens of minutes. The expected figure files are: output/figure10.pdf output/figure10_full_range.pdf output/figure11.pdf output/figure11_full_range.pdf output/figure12.pdf output/figure13.pdf output/figure14.pdf output/figure15.pdf output/figure17.pdf output/figure18d.pdf Figure 17 generates 1000 random instances at each term count with fixed seed 7. Figure 18(d) evolves the packaged Hamiltonians with four product-formula steps at times 0.4, 0.8, and 1.0, then reports the maximum bipartite entropy over every MPS prefix cut. Because the packaged structural Hamiltonians contain Pauli strings without numeric coefficients, every input term uses coefficient Open markers are the dimension-derived bound S_MPS <= floor(n_qubits / 2). The tables are written to results/analysis/figure17.csv and results/analysis/figure18d.csv. Layout benchmarks/ source Hamiltonians hardware/ superconducting and neutral-atom architectures src/gadir_frontend/ architecture-independent GadIR frontend src/superconducting/ Figures 10--11 backend src/neutral_atom/ Figure 12 backend src/ftqc/ Figures 13--14 backend src/mbqc/ Figure 15 backend src/analysis/ Figures 17 and 18(d) workflows baselines/ baseline implementations or dependency records environments/ pinned Conda specifications results/ generated tables and intermediate circuits output/ generated PDFs GadIR licensing is not declared by a new license file in this AE directory. Redistributed third-party code retains its upstream notice in baselines/<method>/LICENSE; TQEC is an installed dependency pinned in environments/analysis.yml.

DOI
10.5281/zenodo.21550064

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Description This artifact contains the core implementation of Kernpiler, a compiler framework for optimizing quantum circuits, and supports full reproducibility of all experimental results presented in the associated paper. The artifact includes all code, data pipelines, and scripts required to regenerate Figures 5–11. System Used for Data Collection NVIDIA A100 GPU with 80GB memory AMD EPYC 9654P 96-core processor x86_64 Linux system Python 3.13.5 Experiments may be computationally intensive, but they are fully parallelizable across multiple devices. Installation Clone or download the repository and navigate to the project directory. Create a virtual environment: python3 -m venv validate source validate/bin/activate Install dependencies: python -m pip install -r requirements.txt Install torch-scatter: python -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-2.10.0+cu128.html Experiment Workflow All experiment scripts are located in: src/compiler/optimization_passes/experiments Each figure can be reproduced by running its corresponding data collection and graphing scripts: Figure 5exp_gatecount_datacollection.py→ graph using:graph_data_scripts/graph_absolute.py Figure 6exp_partition_scaling_datacollection_o1exp_partition_scaling_datacollection→ graph using:graph_data_scripts/graph_o1_o2_side_by_side.py Figure 7exp_runtime_per_pass.py→ graph using:graph_data_scripts/graph_runtime_per_pass.py Figure 8exp_partition_scaling_datacollectiono1_phoenixFT→ graph using:graph_data_scripts/graph_firstorder_scalingFT.py Figure 9exp_partitionalgvsrandom.py→ graph using:graph_data_scripts/graph_partition_vs_random.py Figure 10exp_scaling_data_rewriteradius.py→ graph using:graph_data_scripts/graph_scalingdata.py Figure 11exp_error_scaling_systemsize.py→ output generated directly (no additional graph script required) Execution Notes All experiments are independent Parallel execution is supported Runtime varies depending on system size and hardware

arXiv:2504.07214•Proceedings - International Symposium on Computer Architecture/Proceedings•Jun 1, 2026•1 citation

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Data repository of the paper "Quantum computational displacement sensing"

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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.

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GadIR artifact evaluation This self-contained directory reproduces Figures 10--15, 17, and 18(d) from the packaged Hamiltonians and generated random instances. It contains one GadIR frontend, four architecture backends, the two analysis workflows, and the baseline code used by the experiments. Full reproduction regenerates the result tables from the raw benchmarks. The bundled Figure 15 table supports plot-only validation and is overwritten by reproduce. Repository: https://github.com/xiyurain/GadIR Archive: https://doi.org/10.5281/zenodo.21550065 Requirements Use a machine with Bash, Git, Conda, a C/C++ toolchain, and stable Rust/Cargo. Intel, AMD, and Apple CPUs are supported. The experiments are CPU-only; at least 16 CPU cores, 32 GB RAM, and 20 GB of free disk are recommended. No GPU or quantum hardware is required. Network access is needed only while installing the environments. Install Miniconda (or Anaconda), Git, and the host compiler first. On Ubuntu/Debian, the host packages can be installed with: sudo apt-get update sudo apt-get install -y build-essential curl git Install Rust with rustup: curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh source "$HOME/.cargo/env" conda --version git --version rustc --version cargo --version Installation Clone the public repository (or unpack the Zenodo archive), enter this directory, and run the installation script: git clone git@github.com:xiyurain/GadIR.git cd GadIR/GadIR_AE ./prepare.sh prepare.sh creates three pinned environments under .envs/: analysis: Python 3.10, Qiskit 1.4.3, Qiskit Aer 0.17.1, PyZX 0.9.0, NumPy, pandas, SciPy, matplotlib, Stim/sinter 1.15.0, PyMatching 2.3.0, TQEC, GraphiQ 0.1.1, and Jupyter Notebook; quclear: Python 3.10 and Qiskit 1.1.1. 2qan: Python 3.9 and Qiskit Terra 0.20.2 (Qiskit 0.36.2). The script also builds and installs the packaged Rustiq Python extension into the analysis environment and installs Tweedledum 1.1.1 without build isolation before Qiskit Terra 0.20.2 in the 2QAN environment. TQEC and GraphiQ are installed at the exact revisions recorded in environments/analysis.yml. GraphiQ is retained for compatibility with the submitted installation check and is installed from the bundled wheel built at the recorded revision without its obsolete Qiskit 0.38 dependency; the Figure 15 workflow uses the packaged GSSynth backend. Manual environment activation is unnecessary. An installation can be checked with: ./.envs/analysis/bin/python -c "import qiskit, pyzx, rustiq, stim, sinter, pymatching, tqec, graphiq" ./.envs/quclear/bin/python -c "import qiskit" ./.envs/2qan/bin/python -c "import qiskit" If SSH access to GitHub is unavailable, download the Zenodo archive instead of cloning. The pinned TQEC revision still requires network access during prepare.sh. Running the artifact All commands are run from GadIR_AE/: ./run.sh smoke ./run.sh reproduce ./run.sh plot smoke runs one small input for each architecture backend and reduced-size Figure 17/18(d) analyses. reproduce compiles all selected benchmarks, writes fresh tables under results/, and draws all eight figures. plot only redraws from the tables already generated under results/. To generate all paper figures from completed AE results, use the provided plot-only notebook: ./.envs/analysis/bin/jupyter notebook generate_all_figures.ipynb The notebook draws Figures 10--15, 17, and 18(d) into output/; run ./run.sh reproduce first. The submitted artifact appendix refers to Figure 18(c) in this sentence. This is a labeling typo: the included analysis and notebook reproduce Figure 18(d). The workflows run in this order: superconducting, neutral atom, FTQC, MBQC, and analysis. Full reproduction is intentionally long: FTQC sampling and all 84 Figure 15 compiler jobs can take hours to days. smoke also executes real compiler/simulator paths and can take tens of minutes. The expected figure files are: output/figure10.pdf output/figure10_full_range.pdf output/figure11.pdf output/figure11_full_range.pdf output/figure12.pdf output/figure13.pdf output/figure14.pdf output/figure15.pdf output/figure17.pdf output/figure18d.pdf Figure 17 generates 1000 random instances at each term count with fixed seed 7. Figure 18(d) evolves the packaged Hamiltonians with four product-formula steps at times 0.4, 0.8, and 1.0, then reports the maximum bipartite entropy over every MPS prefix cut. Because the packaged structural Hamiltonians contain Pauli strings without numeric coefficients, every input term uses coefficient Open markers are the dimension-derived bound S_MPS <= floor(n_qubits / 2). The tables are written to results/analysis/figure17.csv and results/analysis/figure18d.csv. Layout benchmarks/ source Hamiltonians hardware/ superconducting and neutral-atom architectures src/gadir_frontend/ architecture-independent GadIR frontend src/superconducting/ Figures 10--11 backend src/neutral_atom/ Figure 12 backend src/ftqc/ Figures 13--14 backend src/mbqc/ Figure 15 backend src/analysis/ Figures 17 and 18(d) workflows baselines/ baseline implementations or dependency records environments/ pinned Conda specifications results/ generated tables and intermediate circuits output/ generated PDFs GadIR licensing is not declared by a new license file in this AE directory. Redistributed third-party code retains its upstream notice in baselines/<method>/LICENSE; TQEC is an installed dependency pinned in environments/analysis.yml.
Description This artifact contains the core implementation of Kernpiler, a compiler framework for optimizing quantum circuits, and supports full reproducibility of all experimental results presented in the associated paper. The artifact includes all code, data pipelines, and scripts required to regenerate Figures 5–11. System Used for Data Collection NVIDIA A100 GPU with 80GB memory AMD EPYC 9654P 96-core processor x86_64 Linux system Python 3.13.5 Experiments may be computationally intensive, but they are fully parallelizable across multiple devices. Installation Clone or download the repository and navigate to the project directory. Create a virtual environment: python3 -m venv validate source validate/bin/activate Install dependencies: python -m pip install -r requirements.txt Install torch-scatter: python -m pip install --no-cache-dir torch-scatter -f https://data.pyg.org/whl/torch-2.10.0+cu128.html Experiment Workflow All experiment scripts are located in: src/compiler/optimization_passes/experiments Each figure can be reproduced by running its corresponding data collection and graphing scripts: Figure 5exp_gatecount_datacollection.py→ graph using:graph_data_scripts/graph_absolute.py Figure 6exp_partition_scaling_datacollection_o1exp_partition_scaling_datacollection→ graph using:graph_data_scripts/graph_o1_o2_side_by_side.py Figure 7exp_runtime_per_pass.py→ graph using:graph_data_scripts/graph_runtime_per_pass.py Figure 8exp_partition_scaling_datacollectiono1_phoenixFT→ graph using:graph_data_scripts/graph_firstorder_scalingFT.py Figure 9exp_partitionalgvsrandom.py→ graph using:graph_data_scripts/graph_partition_vs_random.py Figure 10exp_scaling_data_rewriteradius.py→ graph using:graph_data_scripts/graph_scalingdata.py Figure 11exp_error_scaling_systemsize.py→ output generated directly (no additional graph script required) Execution Notes All experiments are independent Parallel execution is supported Runtime varies depending on system size and hardware
This software-framework note archives scpn-quantum-control, a reproducible Kuramoto--XY quantum-control workflow combining Python/Qiskit orchestration, Rust acceleration, hardware artefact packaging, and publication-grade analysis surfaces. The software supports construction of heterogeneous oscillator networks, XY-Hamiltonian mapping, topology-informed ansatz generation, simulator and hardware execution, and reproducible post-processing of IBM artefacts. The record documents the framework as a research software artefact rather than a standalone physics claim. It highlights the repository structure, reproducibility workflow, accelerated kernels, benchmark and parity checks, and the relationship between source code, generated artefacts, and paper records. The archive includes the JOSS-style manuscript source, bibliography, preview TeX, and rebuilt PDF. Repository field and DOI metadata link the software note back to https://github.com/anulum/scpn-quantum-control.
This deposit contains a reproducible replication package and raw results for a multi-topology hardware validation of Topological Cyclotomic Resonance (TCR) on IBM Quantum backends ibm_fez and ibm_torino. We test the closed-form parameter ruleλ*(V) = sin(π/(2V+1))across five circuit topologies (ring, star, mesh, tree, line) and three problem sizes (V=10, 15, 20), using an ABBA interleaved design for drift control. The package includes raw and summary JSON outputs, a SHA256 manifest, analysis tables, and code/configuration needed to reproduce the experiment (tokens are not included; IBM credentials are provided via environment variables).DOI: 10.5281/zenodo.18357071 | OSF: https://osf.io/xuebt/ | ORCID: https://orcid.org/0009-0007-7286-3373
This methods preprint describes the software and experimental workflow behind scpn-quantum-control, an open-source framework for mapping heterogeneous Kuramoto-type oscillator networks to XY spin Hamiltonians, compiling topology-informed variational ansatze, benchmarking simulation paths, and executing reproducible NISQ hardware experiments. The workflow combines Python/Qiskit orchestration with a Rust/PyO3 acceleration layer for hot-path kernels including coupling-matrix construction, Hamiltonian assembly, trajectory features, hybrid digital--analog partitioning, and protected-subspace diagnostics. The manuscript reports artefact-generated timings across local workstation, ML350, Vertex AI CPU, and Vertex AI Tesla T4 contexts, with checksum parity across Python, Rust, and Go kernels. The record includes manuscript source, notes bibliography, and PDF. It is an archival methods record for the repository and its reproducibility artefacts, not an independent claim of broad quantum advantage.
QECTOR Decoder Benchmark Datasets & Empirical Reports This repository hosts the evolving collection of reproducible benchmark datasets, JSON measurement artifacts, and empirical performance reports for the QECTOR Decoder v3 platform (Rust/Python quantum error-correction decoder). Each versioned deposit contains environment-stamped, content-hashed JSON artifacts together with the corresponding publication-grade report. All numerical results are fully regenerable from the published wheels and the scripts listed in the reproducibility sections. Scope across versions Code-capacity and circuit-level (Stim rotated_memory_z) evaluations Throughput (shots/s, measure-s/shot), logical error rates with 95 % Wilson intervals, syndrome faithfulness (GF(2) exact check), and CUDA-vs-CPU bit identity Decoder families: FastUnionFind, UnionFind, Blossom (exact MWPM), SparseBlossom, Batch/CPUBatch, BPOSD, HybridCascade, AmbiguityCluster, LookupTable, and CUDABatch (unweighted + DEM-weighted) Hardware provenance, licence-tier disclosure (GPU gated behind Enterprise), and SHA-256 inventories of every backing artifact Version history (selected) v0.6.6 (July 2026) – Workbench Benchmark Master Report: 1 858 decode-timing measurements from 105 runs, six code topologies (Heavy Hex, Repetition, Ring, Rotated Surface, Toric, Unrotated Surface), fixed p = 0.05, distances d = 3–19. Focus on raw latency, harmonic-mean throughput and peak memory. v0.6.8 - Empirical edge-hardware benchmarks. v0.7.0 (August 2026) – Full Empirical Decoder Benchmark Report on the published wheel: nine CPU families + CUDA batch path, circuit-level LER parity with PyMatching 2.4.0, exact syndrome faithfulness, DEM-weighted GPU scaling restoration, and complete artifact inventory (Appendix A). All figures and tables in the accompanying reports are regenerated from the JSON files listed with their SHA-256 hashes; no pre-release numbers are carried forward. Related resources PyPI: qector-decoder-v3 Repository: https://github.com/GuillaumeLessard/qector-decoder Website: https://www.qector.store
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.