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.
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.
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 preprint presents a Kuramoto--XY synchronisation study with heterogeneous natural frequencies, simulator baselines, and ledgered IBM Heron r2 evidence. A Rust/PyO3-assisted pipeline supports entanglement entropy, Krylov complexity, OTOC scrambling, and Floquet discrete-time-crystal diagnostics for 2--16 qubit systems. The manuscript reports finite-size diagnostics consistent with Berezinskii--Kosterlitz--Thouless scaling in the small systems studied, including a periodic-boundary central-charge estimate near c = 1 at n = 8, while explicitly noting finite-size drift at larger sizes. It also records subharmonic Floquet response, non-ergodic level-spacing behaviour for heterogeneous frequencies, and a hardware-anchored scaling analysis that bounds exact Hilbert-space simulation costs. The record archives the manuscript source and PDF. Legacy IBM artefacts are treated as committed artefact-path evidence rather than aggregate validation, and the resource-boundary discussion is not promoted as a broad quantum-advantage claim because classical Rust Kuramoto ODE baselines remain faster in the measured observable-level regime.
Hybrid quantum-high performance computing (Q-HPC) workflows are emerging as a key strategy for running quantum applications at scale in current noisy intermediate-scale quantum (NISQ) devices. These workflows must operate seamlessly across diverse simulators and hardware backends since no single simulator offers the best performance for every circuit type. Simulation efficiency depends strongly on circuit structure, entanglement, and depth, making a flexible and backend-agnostic execution model essential for fair benchmarking, informed platform selection, and ultimately the identification of quantum advantage opportunities. In this work, we extend the Quantum Framework (QFw), a modular and HPC-aware orchestration layer, to integrate multiple local backends (Qiskit Aer, NWQ-Sim, QTensor, and TN-QVM) and a cloud-based quantum backend (IonQ) under a unified interface. Using this integration, we execute a number of non-variational as well as variational workloads. The results highlight workload-specific backend advantages: while Qiskit Aer’s matrix product state excels for large Ising models, NWQ-Sim not only leads on large-scale entanglement and Hamiltonian but also shows the benefits of concurrent subproblem execution in a distributed manner for optimization problems. These findings demonstrate that simulator-agnostic, HPC-aware orchestration is a practical path toward scalable, reproducible, and portable Q-HPC ecosystems, thereby accelerating progress toward demonstrating quantum advantage.
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.
This preprint introduces and characterises a collective Fisher-information-inspired magnetisation-feedback term for Kuramoto--XY quantum simulations. The model augments a heterogeneous XY Hamiltonian with a diagonal term proportional to minus lambda times total magnetisation squared, normalised by qubit count, creating explicit magnetisation-sector energy shifts while preserving ideal XY magnetisation conservation. Artefact-generated exact diagonalisation on n = 4, 6, and 8 qubits shows systematic changes in ground energy, spectral width, and spectral gap as the feedback strength varies. The manuscript also reports adjacent-gap statistics, low-energy bipartition entropy trends, and a small n = 4 VQE benchmark comparing a K_ij-informed ansatz against generic TwoLocal and EfficientSU2 baselines. The record archives the source, bibliography, and PDF for a hardware-falsifiable modelling contribution. Claims are limited to the stated exact diagonalisation grid, VQE benchmark configuration, and associated committed artefacts.
QECTOR-Decoder-v3 is a high-throughput, multi-backend quantum error correction (QEC) decoding suite implemented in compiled native Rust with native Python bindings via PyO3. Designed for high-performance computing (HPC) environments, low-latency quantum hardware pipelines, and offline zero-egress research, the engine bridges theoretical stabilizer formalisms with hardware-level decoding execution. This archival release includes the formal technical specification, architectural reference manual, cryptographic proof suite, and reproducible Linux/x86_64 verification harness for version 1.0.0. Key Architectural Components Native Compiled Core: High-performance Rust backend exposing zero-copy FFI bindings to Python (CPython 3.9–3.13) via PyO3. Multi-Backend Decoding Library: Includes implementations of Blossom Minimum Weight Perfect Matching (MWPM), FastUnionFind, Belief Propagation with Ordered Statistics Decoding (BP-OSD), Belief-Matching, and an AutoDecoder multi-tier fallback architecture. Hardware Interoperability: Drop-in compatibility with standard QEC formats and frameworks (Stim, PyMatching, Sinter) with Rayon multi-threading and CUDA/OpenCL acceleration hooks. Model Context Protocol (MCP) Integration: Native stdio JSON-RPC tool surfaces supporting automated syndrome decoding, check-matrix validation, and real-time telemetry profiling. Included Proof Artifacts & Verification Bundle This release contains the complete, cryptographic proof suite validating numerical correctness, memory layout, and statistical bounds: certified_proof.json & certified_proof.json.sha256: Tamper-evident execution logs confirming 100% syndrome faithfulness (H⋅c=s) across all evaluation checkpoints. wilson_interval.png / .svg: Exact 95% Wilson score confidence interval statistical sweeps across code distance ranges (d=3 through d=7). syndrome_faithfulness.png / .svg: High-resolution visual parity validation and check-matrix verification charts. workspace_strides.png / .svg: Cache-line memory alignment and SIMD stride profiling diagnostics. certified_report.pdf & certified_report.md: Formal verification report and reproduction methodology. verify_artifacts.py & test_qector_decoder_v3_proofs_linux.py: Automated Python/Linux validation scripts to independently recompute and verify SHA-256 signatures and artifact integrity. Reproduction & Verification To verify the included artifact bundle locally: Bash python3 verify_artifacts.py python3 test_qector_decoder_v3_proofs_linux.py Licensing & Metadata License: PolyForm Noncommercial / Proprietary Commercial (iD01t Productions) PyPI Distribution: qector-decoder-v3 Keywords: Quantum Error Correction, Surface Codes, Rust, PyO3, Stabilizer Decoders, MWPM, BP-OSD, Union-Find, Fault-Tolerant Quantum Computing, Model Context Protocol (MCP). License terms. The software, scripts, and code in this record (including the Python verification scripts and the inlined pure-Python ground truth) are licensed under the PolyForm Noncommercial License 1.0.0: noncommercial use only. Any commercial use requires a separate commercial license from iD01t Productions. Documentation, reports, and figures in this record are made available under the Creative Commons Attribution 4.0 International License (CC-BY-4.0) with credit to Guillaume Lessard / iD01t Productions. Copyright (c) 2026 Guillaume Lessard / iD01t Productions. All rights reserved except as expressly granted above.