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.github/workflows/test.yml

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workflow_dispatch:
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# push:
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# branches:
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# pull_request:
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# types:
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# - opened
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# - synchronize
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push:
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branches:
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README.md

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# OMMX Quantum Benchmarks
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OMMX Quantum Benchmarks provides access to quantum optimization benchmark datasets in [OMMX](https://jij-inc.github.io/ommx/en/introduction.html) format for easier integration with quantum and classical optimization workflows.
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Documentation: https://jij-inc.github.io/QmmxQuantumBenchmarks/
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Documentation: https://jij-inc.github.io/OmmxQuantumBenchmarks/
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## Quick Start
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### Installation

docs/en/_config.yml

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docs/en/_toc.yml

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- caption: "Release Notes"
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chapters:
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- file: release_note/index
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# - caption: "Release Notes"
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# chapters:
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# - file: release_note/index

docs/en/release_note/index.ipynb

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"cell_type": "markdown",
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"source": [
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"# Release Notes\n",
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"\n",
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"## Version 0.1.0\n",
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"\n",
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"**Initial Release - QOBLIB Dataset Collection**\n",
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"\n",
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"### Overview\n",
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"This initial release establishes OMMX Quantum Benchmarks as a collection of optimization benchmark datasets in OMMX format. The first collection includes selected datasets from the [QOBLIB (Quantum Optimization Benchmarking Library)](https://git.zib.de/qopt/qoblib-quantum-optimization-benchmarking-library).\n",
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"\n",
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"**Scope**: This release focuses on QOBLIB datasets, with framework designed to accommodate additional benchmark sources in future releases.\n",
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"\n",
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"### Features\n",
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"- Python API for accessing quantum optimization benchmark datasets\n",
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"- OMMX format conversion and standardization\n",
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"- GitHub Container Registry integration for distributed data access\n",
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"- Consistent interface across all dataset categories\n",
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"\n",
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"### Currently Available Data\n",
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"**From QOBLIB Collection**:\n",
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"- **Marketsplit** (01): 160+ instances with binary linear and unconstrained formulations\n",
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"- **Labs** (02): 99 instances with integer and quadratic unconstrained formulations\n",
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"\n",
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"### Attribution\n",
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"All converted data is derived from the original QOBLIB repository created by Thorsten Koch, David E. Bernal Neira, and colleagues, licensed under CC BY 4.0."
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]
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"source": "# Release Notes\n\n## Version 0.1.0\n\n**Initial Release - QOBLIB Dataset Collection**\n\n### Overview\nThis initial release establishes OMMX Quantum Benchmarks as a collection of optimization benchmark datasets in OMMX format. The first collection includes selected datasets from the [QOBLIB (Quantum Optimization Benchmarking Library)](https://git.zib.de/qopt/qoblib-quantum-optimization-benchmarking-library).\n\n**Scope**: This release focuses on QOBLIB datasets, with framework designed to accommodate additional benchmark sources in future releases.\n\n### Features\n- Python API for accessing quantum optimization benchmark datasets\n- OMMX format conversion and standardization\n- GitHub Container Registry integration for distributed data access\n- Consistent interface across all dataset categories\n\n### Currently Available Data\n**From QOBLIB Collection**:\n- **Labs** (02): 99 instances with integer and quadratic unconstrained formulations\n- **Birkhoff** (03): 800 instances with integer linear formulations\n- **Steiner** (04): 31 instances with integer linear formulations\n- **Topology** (10): 16 instances with flow MIP, Seidel linear, and Seidel quadratic formulations\n\n### Attribution\nAll converted data is derived from the original QOBLIB repository created by Thorsten Koch, David E. Bernal Neira, and colleagues, licensed under CC BY 4.0."
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docs/en/tutorial/usage/dataset_overview.ipynb

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"\n",
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"**Problem Type**: Laboratory assignment problems \n",
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"**Models**: Integer, Quadratic unconstrained \n",
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"**Instances**: 99 instances (labs002 - labs100)\n",
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"\n",
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"Laboratory problems typically involve resource allocation and scheduling in laboratory environments."
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"**Instances**: 99 instances (labs002 - labs100)"
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{
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"source": [
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"## Birkhoff (`03_birkhoff`)\n",
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"\n",
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"**Problem Type**: Birkhoff polytope problems \n",
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"**Models**: Integer linear \n",
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"**Instances**: 800 instances (bhD-3-001 - bhS-6-100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "yt34ivrq2v",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Available models: ['integer_linear']\n",
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"integer_linear: 800 instances\n"
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]
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}
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],
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"source": [
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"from ommx_quantum_benchmarks.qoblib import Birkhoff\n",
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"\n",
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"dataset = Birkhoff()\n",
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"print(f\"Available models: {dataset.model_names}\")\n",
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"for model in dataset.model_names:\n",
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" instances = dataset.available_instances[model]\n",
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" print(f\"{model}: {len(instances)} instances\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "srqvnex133",
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"metadata": {},
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"source": [
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"## Steiner (`04_steiner`)\n",
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"\n",
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"**Problem Type**: Steiner tree problems \n",
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"**Models**: Integer linear \n",
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"**Instances**: 31 instances (stp_s020_l2_t3_h2_rs24098 - stp_s040_l2_t4_h3_rs123)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "2rs94wyke07",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Available models: ['integer_linear']\n",
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"integer_linear: 31 instances\n"
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]
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}
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],
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"source": [
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"from ommx_quantum_benchmarks.qoblib import Steiner\n",
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"\n",
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"dataset = Steiner()\n",
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"print(f\"Available models: {dataset.model_names}\")\n",
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"for model in dataset.model_names:\n",
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" instances = dataset.available_instances[model]\n",
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" print(f\"{model}: {len(instances)} instances\")"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Topology (`10_topology`)\n",
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"\n",
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"**Problem Type**: Network topology optimization \n",
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"**Models**: Flow MIP, Seidel linear, Seidel quadratic \n",
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"**Instances**: 16 instances per model (topology_15_3 - topology_50_4)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "gngmnigjefn",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Available models: ['flow_mip', 'seidel_linear', 'seidel_quadratic']\n",
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"flow_mip: 16 instances\n",
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"seidel_linear: 16 instances\n",
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"seidel_quadratic: 16 instances\n"
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]
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}
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],
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"source": [
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"from ommx_quantum_benchmarks.qoblib import Topology\n",
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"\n",
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"dataset = Topology()\n",
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"print(f\"Available models: {dataset.model_names}\")\n",
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"for model in dataset.model_names:\n",
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" instances = dataset.available_instances[model]\n",
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" print(f\"{model}: {len(instances)} instances\")"
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]
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},
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{
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"metadata": {},
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"## Other Dataset Categories\n",
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"\n",
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"The following dataset categories are defined in the framework but currently contain no instances. These represent problem types that may be expanded in future releases:\n",
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"- **Market Split** (`01_marketsplit`) - Binary linear and binary unconstrained market split problems\n",
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"- **Birkhoff** (`03_birkhoff`) - Integer linear problems related to doubly stochastic matrices\n",
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"- **Steiner** (`04_steiner`) - Integer linear Steiner tree problems\n",
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"- **Sports** (`05_sports`) - Mixed integer linear sports scheduling problems\n",
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"- **Portfolio** (`06_portfolio`) - Binary quadratic and quadratic unconstrained portfolio optimization\n",
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"- **Independent Set** (`07_independent_set`) - Binary linear and unconstrained graph problems\n",
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"- **Network** (`08_network`) - Integer linear network optimization\n",
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"- **Routing** (`09_routing`) - Integer linear vehicle routing problems \n",
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"- **Topology** (`10_topology`) - Network topology problems with multiple formulations\n",
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"- **Routing** (`09_routing`) - Integer linear vehicle routing problems\n",
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"**Note**: These datasets can be instantiated but will return empty instance lists. Check the `available_instances` property to see current availability.\n",
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"|---------|--------|----------------|---------|\n",
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"| Marketsplit | 2 | 0 | 🚧 Defined, no instances |\n",
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"| Labs | 2 | 99 | ✅ Available |\n",
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"| Birkhoff | 1 | 0 | 🚧 Defined, no instances |\n",
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"| Steiner | 1 | 0 | 🚧 Defined, no instances |\n",
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"| Birkhoff | 1 | 800 | ✅ Available |\n",
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"| Steiner | 1 | 31 | ✅ Available |\n",
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"| Sports | 1 | 0 | 🚧 Defined, no instances |\n",
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"| Portfolio | 2 | 0 | 🚧 Defined, no instances |\n",
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"| IndependentSet | 2 | 0 | 🚧 Defined, no instances |\n",
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"| Network | 1 | 0 | 🚧 Defined, no instances |\n",
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"| Routing | 1 | 0 | 🚧 Defined, no instances |\n",
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"| Topology | 3 | 0 | 🚧 Defined, no instances |\n",
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"| Topology | 3 | 16 | ✅ Available |\n",
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docs/ja/_config.yml

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docs/ja/_toc.yml

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# - caption: "リリースノート"
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# chapters:
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# - file: release_note/index

docs/ja/release_note/index.ipynb

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"# リリースノート\n",
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"## バージョン 0.1.0\n",
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"**初回リリース - QOBLIBデータセットコレクション**\n",
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"\n",
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"### 概要\n",
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"この初回リリースでは、OMMX Quantum BenchmarksをOMMX形式の最適化ベンチマークデータセットのコレクションとして確立します。最初のコレクションには、[QOBLIB(Quantum Optimization Benchmarking Library)](https://git.zib.de/qopt/qoblib-quantum-optimization-benchmarking-library)から選択されたデータセットが含まれています。\n",
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"**範囲**: このリリースはQOBLIBデータセットに焦点を当てており、将来のリリースで追加のベンチマークソースに対応するためのフレームワークが設計されています。\n",
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"### 機能\n",
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"- 量子最適化ベンチマークデータセットにアクセスするためのPython API\n",
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"- OMMX形式の変換と標準化\n",
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"- 分散データアクセスのためのGitHub Container Registry統合\n",
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"- 全データセットカテゴリにわたる一貫したインターフェース\n",
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"\n",
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"### 現在利用可能なデータ\n",
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"**QOBLIBコレクションから**:\n",
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"- **Labs** (02): 整数および二次制約なし定式化による99インスタンス\n",
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"\n",
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"### 帰属\n",
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"変換されたすべてのデータは、Thorsten Koch、David E. Bernal Neira、および同僚によって作成された元のQOBLIBリポジトリから派生しており、CC BY 4.0ライセンスの下で提供されています。"
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"source": "# リリースノート\n\n## バージョン 0.1.0\n\n**初回リリース - QOBLIBデータセットコレクション**\n\n### 概要\nこの初回リリースでは、OMMX Quantum BenchmarksをOMMX形式の最適化ベンチマークデータセットのコレクションとして確立します。最初のコレクションには、[QOBLIB(Quantum Optimization Benchmarking Library)](https://git.zib.de/qopt/qoblib-quantum-optimization-benchmarking-library)から選択されたデータセットが含まれています。\n\n**範囲**: このリリースはQOBLIBデータセットに焦点を当てており、将来のリリースで追加のベンチマークソースに対応するためのフレームワークが設計されています。\n\n### 機能\n- 量子最適化ベンチマークデータセットにアクセスするためのPython API\n- OMMX形式の変換と標準化\n- 分散データアクセスのためのGitHub Container Registry統合\n- 全データセットカテゴリにわたる一貫したインターフェース\n\n### 現在利用可能なデータ\n**QOBLIBコレクションから**:\n- **Labs** (02): 整数および二次制約なし定式化による99インスタンス\n- **Birkhoff** (03): 整数線形定式化による800インスタンス\n- **Steiner** (04): 整数線形定式化による31インスタンス\n- **Topology** (10): フローMIP、Seidel線形、Seidel二次定式化による16インスタンス\n\n### 帰属\n変換されたすべてのデータは、Thorsten Koch、David E. Bernal Neira、および同僚によって作成された元のQOBLIBリポジトリから派生しており、CC BY 4.0ライセンスの下で提供されています。"
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