[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-tao-train-metric-learning-recognition":3,"mdc--lg0z5c-key":31,"related-org-nvidia-tao-train-metric-learning-recognition":1580,"related-repo-nvidia-tao-train-metric-learning-recognition":1738},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"tao-train-metric-learning-recognition","train metric-learning models for visual recognition","Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition) using triplet \u002F contrastive losses. Use when training, evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include \"train metric learning\", \"ml-recog\", \"retrieval embeddings\", \"triplet loss recognition\", \"fine-grained matching\".",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"nvidia","NVIDIA","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fnvidia.png",[12,16,17],{"name":13,"slug":14,"type":15},"Deep Learning","deep-learning","tag",{"name":9,"slug":8,"type":15},{"name":18,"slug":19,"type":15},"Computer Vision","computer-vision",2473,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills","2026-07-14T05:27:58.129928","Apache-2.0",281,[],{"repoUrl":21,"stars":20,"forks":24,"topics":27,"description":28},[],"AI agent skills published by NVIDIA","https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Ftao-train-metric-learning-recognition","---\nname: tao-train-metric-learning-recognition\ndescription: Metric-learning recognition (ml-recog) for fine-grained visual recognition. Learns embeddings for\n  retrieval-based matching (e.g., retail product recognition) using triplet \u002F contrastive losses. Use when training,\n  evaluating, exporting, or running inference for a TAO metric-learning recognition model. Trigger phrases include\n  \"train metric learning\", \"ml-recog\", \"retrieval embeddings\", \"triplet loss recognition\", \"fine-grained matching\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit.\nmetadata:\n  version: \"0.1.0\"\n  author: NVIDIA Corporation\nallowed-tools: Read Bash\ntags:\n- metric\n- learning\n- recognition\n---\n\n# ML Recog\n\nMetric learning recognition for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition). Uses triplet\u002Fcontrastive losses.\n\nSet model.pretrained_model_path for pretrained backbone.\n\nFor TAO Deploy TensorRT actions (`gen_trt_engine`, TensorRT `evaluate`, and TensorRT `inference`), read `references\u002Ftao-deploy-metric-learning-recognition.md` first. Deploy spec templates live in this skill's `references\u002F` folder with the `spec_template_deploy_*.yaml` prefix.\n\n## Dataclass Schemas\n\nGenerated TAO Core schemas are packaged in `schemas\u002F\u003Caction>.schema.json`, with `schemas\u002Fmanifest.json` listing available actions. Each generated schema also emits `references\u002Fspec_template_\u003Caction>.yaml` from the schema top-level `default` field. AutoML enablement is declared at the model layer in `references\u002Fskill_info.yaml` via `automl_enabled`. Runnable AutoML still requires `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` to exist and parse. Use the packaged train schema for `automl_default_parameters`, `automl_disabled_parameters`, defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect `~\u002Ftao-core` at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.\n\n## Train Action Policy\n\nThis model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references\u002Fskill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` \u002F `off` in new launch prompts. Treat phrases like \"turn off AutoML\", \"disable AutoML\", \"no HPO\", or \"plain training\" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas\u002Ftrain.schema.json` and `references\u002Fspec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow\u002Fapplication overrides for datasets, specs, output directories, GPU\u002Fplatform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.\n\nNon-train actions such as `evaluate`, `inference`, `export`, and deploy flows stay in this model skill. The per-run `automl_policy` override does not change model metadata.\n\n## Training Requirements\n\n- **Dataset type:** ml_recog\n- **Formats:** default\n- **Monitoring metric:** val Precision at Rank 1\n\n### Per-Action Dataset Requirements\n\n| Action | Spec Key | Source | Files | List? |\n|---|---|---|---|---|\n| evaluate | dataset.val_dataset | train_datasets | reference: metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Freference.tar.gz, query: metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Ftest.tar.gz | No |\n| inference | dataset.val_dataset | train_datasets | reference: metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Freference.tar.gz, query:  | No |\n| inference | inference.input_path | train_datasets | metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Ftest.tar.gz | No |\n| train | dataset.train_dataset | train_datasets | metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Ftrain.tar.gz | No |\n| train | dataset.val_dataset | train_datasets | reference: metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Freference.tar.gz, query: metric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Fval.tar.gz | No |\n\n### Typical Spec Overrides\n\nData source overrides are **mandatory for every action** — the agent MUST construct data source paths from the Per-Action Dataset Requirements table above and include them in `spec_overrides`.\n\n```python\nS3_TRAIN = \"s3:\u002F\u002Fbucket\u002Fdata\u002Ftrain\"\n```\n\n**train (mandatory data sources):**\n```python\n{\n    \"train.num_epochs\": 30,\n    \"train.checkpoint_interval\": 10,\n    \"train.validation_interval\": 10,\n    \"train.num_gpus\": 1,\n    \"dataset.train_dataset\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Ftrain.tar.gz\",\n    \"dataset.val_dataset\": {\"reference\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Freference.tar.gz\", \"query\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Fknown_classes\u002Fval.tar.gz\"},\n}\n```\n\n**evaluate (mandatory data sources):**\n```python\n{\n    \"evaluate.checkpoint\": \"\u003Cselected train\u002FAutoML checkpoint>\",\n    \"dataset.val_dataset\": {\"reference\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Freference.tar.gz\", \"query\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Ftest.tar.gz\"},\n}\n```\n\n**inference (mandatory data sources):**\n```python\n{\n    \"inference.checkpoint\": \"\u003Cselected train\u002FAutoML checkpoint>\",\n    \"dataset.val_dataset\": {\"reference\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Freference.tar.gz\"},\n    \"inference.input_path\": f\"{S3_TRAIN}\u002Fmetric_learning_recognition\u002Fretail-product-checkout-dataset_classification_demo\u002Funknown_classes\u002Ftest.tar.gz\",\n}\n```\n## Eval Dataset\n\nRequired. Evaluation requires reference and query datasets for retrieval metrics.\n\n## Important Parameters\n\n- **model.backbone**: Default resnet_50. Options: resnet_50, resnet_101, fan_small, fan_base, fan_large, fan_tiny, nvdinov2_vit_large_legacy.\n- **model.feat_dim**: Embedding dimension. Default 256. Output feature vector size for similarity matching.\n- **train.batch_size**: Per-GPU batch size. Default 4. `val_batch_size` also 4. For training and AutoML search, `train.batch_size` must be divisible by `dataset.num_instance`.\n- **dataset.num_instance**: Instances per identity in a batch (P\u002FK sampling). Default 4. Controls how many images of the same class appear together. If using a custom AutoML range for `train.batch_size`, use explicit options that are multiples of this value.\n- **train.optim.trunk.base_lr**: Learning rate for the trunk (backbone). Default 3.5e-4 (Adam).\n- **train.optim.embedder.base_lr**: Learning rate for the embedding head. Default 3.5e-4.\n- **train.optim.triplet_loss_margin**: Margin for triplet loss. Default 0.3. smooth_loss=True by default.\n- **train.optim.miner_function_margin**: Hard mining margin. Default 0.1. Controls pair mining difficulty.\n- **train.optim.steps**: LR decay steps. Default [40, 70] with gamma=0.1.\n- **dataset.train_dataset**: Path to training images organized in class folders.\n- **dataset.val_dataset**: Dict with 'reference' and 'query' keys pointing to ImageNet-format directories for retrieval evaluation.\n\n## Multi-GPU \u002F Multi-Node\n\n**Launch method:** Lightning-managed (single `python` process, Lightning spawns workers).\n\n| Spec Key | Description | Default |\n|----------|-------------|---------|\n| `train.num_gpus` | Number of GPUs | 1 |\n| `train.gpu_ids` | GPU device indices | [0] |\n\n- Strategy: `auto` (Lightning picks best strategy automatically)\n- No explicit `num_nodes` or `distributed_strategy` config — single-node oriented\n\n## Hardware\n\nMinimum 1 GPU(s), recommended 2 GPU(s). 16GB+ VRAM per GPU. Metric learning benefits from larger batch sizes for better triplet sampling but is otherwise moderate on memory.\n\n## Error Patterns\n\n**Reference\u002Fquery mismatch**: Ensure reference and query datasets share compatible class namespaces for evaluation.\n\n**PyTorch 2.6 checkpoint load failure on checkpoint actions**: Current TAO\nML-Recog checkpoints may contain OmegaConf objects. For checkpoints produced by\nthe same trusted TAO train\u002FAutoML workflow, set\n`TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1` in downstream evaluate, inference, export,\nor resume\u002Fretrain job env vars so Lightning can load the full checkpoint. Do not\nuse this env var for untrusted checkpoints.\n\n## Spec Param \u002F Parent Model Inference\n\nModel-specific inference mappings belong in this MD file, not in `config.json`. Generated runners should read this section and apply the mappings with SDK helpers before `create_job()`. This mirrors the old microservices `infer_params.py` flow.\n\nInference mappings from TAO Core `ml_recog.config.json`:\n\n| Action | Spec Field | Inference Function | Meaning |\n|---|---|---|---|\n| evaluate | `evaluate.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| evaluate | `results_dir` | `output_dir` | current job results directory |\n| export | `export.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| export | `export.onnx_file` | `create_onnx_file` | output ONNX path |\n| export | `results_dir` | `output_dir` | current job results directory |\n| inference | `inference.checkpoint` | `parent_model` | model file inferred from the parent job results folder |\n| inference | `results_dir` | `output_dir` | current job results directory |\n| train | `model.pretrained_model_path` | `ptm_if_no_resume_model` | PTM when no resume checkpoint exists |\n| train | `results_dir` | `output_dir` | current job results directory |\n| train | `train.resume_training_checkpoint_path` | `resume_model` | model file inferred from the current job results folder |\n\nFor `parent_model` or `parent_model_folder`, pass the upstream train\u002Fexport\u002FAutoML child job id as `parent_job_id`. The SDK lists the parent result folder, filters checkpoint artifacts, and returns the selected model file or folder. Do not add these mappings back to `config.json` and do not patch generated runner scripts to guess checkpoint paths.\n\n## Deployment\n\n- [tao-deploy-metric-learning-recognition](references\u002Ftao-deploy-metric-learning-recognition.md)\n",{"data":32,"body":42},{"name":4,"description":6,"license":23,"compatibility":33,"metadata":34,"allowed-tools":37,"tags":38},"Requires docker + nvidia-container-toolkit.",{"version":35,"author":36},"0.1.0","NVIDIA Corporation","Read Bash",[39,40,41],"metric","learning","recognition",{"type":43,"children":44},"root",[45,54,60,65,119,126,219,225,334,366,372,408,415,584,590,610,631,639,717,725,762,770,815,821,826,832,978,984,1001,1074,1111,1117,1122,1128,1138,1156,1162,1191,1204,1522,1556,1562,1574],{"type":46,"tag":47,"props":48,"children":50},"element","h1",{"id":49},"ml-recog",[51],{"type":52,"value":53},"text","ML Recog",{"type":46,"tag":55,"props":56,"children":57},"p",{},[58],{"type":52,"value":59},"Metric learning recognition for fine-grained visual recognition. Learns embeddings for retrieval-based matching (e.g., retail product recognition). Uses triplet\u002Fcontrastive losses.",{"type":46,"tag":55,"props":61,"children":62},{},[63],{"type":52,"value":64},"Set model.pretrained_model_path for pretrained backbone.",{"type":46,"tag":55,"props":66,"children":67},{},[68,70,77,79,85,87,93,95,101,103,109,111,117],{"type":52,"value":69},"For TAO Deploy TensorRT actions (",{"type":46,"tag":71,"props":72,"children":74},"code",{"className":73},[],[75],{"type":52,"value":76},"gen_trt_engine",{"type":52,"value":78},", TensorRT ",{"type":46,"tag":71,"props":80,"children":82},{"className":81},[],[83],{"type":52,"value":84},"evaluate",{"type":52,"value":86},", and TensorRT ",{"type":46,"tag":71,"props":88,"children":90},{"className":89},[],[91],{"type":52,"value":92},"inference",{"type":52,"value":94},"), read ",{"type":46,"tag":71,"props":96,"children":98},{"className":97},[],[99],{"type":52,"value":100},"references\u002Ftao-deploy-metric-learning-recognition.md",{"type":52,"value":102}," first. Deploy spec templates live in this skill's ",{"type":46,"tag":71,"props":104,"children":106},{"className":105},[],[107],{"type":52,"value":108},"references\u002F",{"type":52,"value":110}," folder with the ",{"type":46,"tag":71,"props":112,"children":114},{"className":113},[],[115],{"type":52,"value":116},"spec_template_deploy_*.yaml",{"type":52,"value":118}," prefix.",{"type":46,"tag":120,"props":121,"children":123},"h2",{"id":122},"dataclass-schemas",[124],{"type":52,"value":125},"Dataclass Schemas",{"type":46,"tag":55,"props":127,"children":128},{},[129,131,137,139,145,147,153,155,161,163,169,171,177,179,185,187,193,195,201,203,209,211,217],{"type":52,"value":130},"Generated TAO Core schemas are packaged in ",{"type":46,"tag":71,"props":132,"children":134},{"className":133},[],[135],{"type":52,"value":136},"schemas\u002F\u003Caction>.schema.json",{"type":52,"value":138},", with ",{"type":46,"tag":71,"props":140,"children":142},{"className":141},[],[143],{"type":52,"value":144},"schemas\u002Fmanifest.json",{"type":52,"value":146}," listing available actions. Each generated schema also emits ",{"type":46,"tag":71,"props":148,"children":150},{"className":149},[],[151],{"type":52,"value":152},"references\u002Fspec_template_\u003Caction>.yaml",{"type":52,"value":154}," from the schema top-level ",{"type":46,"tag":71,"props":156,"children":158},{"className":157},[],[159],{"type":52,"value":160},"default",{"type":52,"value":162}," field. AutoML enablement is declared at the model layer in ",{"type":46,"tag":71,"props":164,"children":166},{"className":165},[],[167],{"type":52,"value":168},"references\u002Fskill_info.yaml",{"type":52,"value":170}," via ",{"type":46,"tag":71,"props":172,"children":174},{"className":173},[],[175],{"type":52,"value":176},"automl_enabled",{"type":52,"value":178},". Runnable AutoML still requires ",{"type":46,"tag":71,"props":180,"children":182},{"className":181},[],[183],{"type":52,"value":184},"schemas\u002Ftrain.schema.json",{"type":52,"value":186}," and ",{"type":46,"tag":71,"props":188,"children":190},{"className":189},[],[191],{"type":52,"value":192},"references\u002Fspec_template_train.yaml",{"type":52,"value":194}," to exist and parse. Use the packaged train schema for ",{"type":46,"tag":71,"props":196,"children":198},{"className":197},[],[199],{"type":52,"value":200},"automl_default_parameters",{"type":52,"value":202},", ",{"type":46,"tag":71,"props":204,"children":206},{"className":205},[],[207],{"type":52,"value":208},"automl_disabled_parameters",{"type":52,"value":210},", defaults, min\u002Fmax bounds, enums, option weights, math conditions, dependencies, and popular parameters. Do not expect ",{"type":46,"tag":71,"props":212,"children":214},{"className":213},[],[215],{"type":52,"value":216},"~\u002Ftao-core",{"type":52,"value":218}," at runtime; maintainers regenerate schemas\u002Ftemplates before packaging the skill bank.",{"type":46,"tag":120,"props":220,"children":222},{"id":221},"train-action-policy",[223],{"type":52,"value":224},"Train Action Policy",{"type":46,"tag":55,"props":226,"children":227},{},[228,230,235,237,243,245,251,253,259,261,267,269,275,277,282,283,289,291,296,297,302,304,310,312,318,320,325,327,332],{"type":52,"value":229},"This model is AutoML-enabled at the model layer. Before handling any train-stage request, read ",{"type":46,"tag":71,"props":231,"children":233},{"className":232},[],[234],{"type":52,"value":168},{"type":52,"value":236}," and resolve the run override from either an explicit ",{"type":46,"tag":71,"props":238,"children":240},{"className":239},[],[241],{"type":52,"value":242},"automl_policy",{"type":52,"value":244}," value or the user's workflow request. Use ",{"type":46,"tag":71,"props":246,"children":248},{"className":247},[],[249],{"type":52,"value":250},"automl_policy: on",{"type":52,"value":252}," by default and only expose ",{"type":46,"tag":71,"props":254,"children":256},{"className":255},[],[257],{"type":52,"value":258},"on",{"type":52,"value":260}," \u002F ",{"type":46,"tag":71,"props":262,"children":264},{"className":263},[],[265],{"type":52,"value":266},"off",{"type":52,"value":268}," in new launch prompts. 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When ",{"type":46,"tag":71,"props":278,"children":280},{"className":279},[],[281],{"type":52,"value":250},{"type":52,"value":202},{"type":46,"tag":71,"props":284,"children":286},{"className":285},[],[287],{"type":52,"value":288},"automl_enabled: true",{"type":52,"value":290},", and both ",{"type":46,"tag":71,"props":292,"children":294},{"className":293},[],[295],{"type":52,"value":184},{"type":52,"value":186},{"type":46,"tag":71,"props":298,"children":300},{"className":299},[],[301],{"type":52,"value":192},{"type":52,"value":303}," are packaged, route the train action through ",{"type":46,"tag":71,"props":305,"children":307},{"className":306},[],[308],{"type":52,"value":309},"tao-skill-bank:tao-run-automl",{"type":52,"value":311}," by default with this model's ",{"type":46,"tag":71,"props":313,"children":315},{"className":314},[],[316],{"type":52,"value":317},"skill_dir",{"type":52,"value":319},". 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Use direct model training only when ",{"type":46,"tag":71,"props":328,"children":330},{"className":329},[],[331],{"type":52,"value":274},{"type":52,"value":333}," or the packaged train schema\u002Ftemplate is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.",{"type":46,"tag":55,"props":335,"children":336},{},[337,339,344,345,350,351,357,359,364],{"type":52,"value":338},"Non-train actions such as ",{"type":46,"tag":71,"props":340,"children":342},{"className":341},[],[343],{"type":52,"value":84},{"type":52,"value":202},{"type":46,"tag":71,"props":346,"children":348},{"className":347},[],[349],{"type":52,"value":92},{"type":52,"value":202},{"type":46,"tag":71,"props":352,"children":354},{"className":353},[],[355],{"type":52,"value":356},"export",{"type":52,"value":358},", and deploy flows stay in this model skill. 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Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1787,1788,1789],{"name":1747,"slug":1748,"type":15},{"name":9,"slug":8,"type":15},{"name":1689,"slug":1690,"type":15},"2026-07-17T05:29:03.913266",{"slug":1792,"name":1792,"fn":1793,"description":1794,"org":1795,"tags":1796,"stars":20,"repoUrl":21,"updatedAt":1805},"amc-run-video-calibration","calibrate video datasets with AutoMagicCalib","Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP\u002Flive streams, use amc-run-rtsp-calibration instead.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1797,1798,1801,1802],{"name":1733,"slug":1734,"type":15},{"name":1799,"slug":1800,"type":15},"Imaging","imaging",{"name":9,"slug":8,"type":15},{"name":1803,"slug":1804,"type":15},"Video","video","2026-07-17T05:28:53.905004",{"slug":1807,"name":1807,"fn":1808,"description":1809,"org":1810,"tags":1811,"stars":20,"repoUrl":21,"updatedAt":1820},"amc-setup-calibration-stack","deploy AutoMagicCalib microservice with Docker","Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1812,1813,1816,1817],{"name":1561,"slug":1558,"type":15},{"name":1814,"slug":1815,"type":15},"Docker","docker",{"name":9,"slug":8,"type":15},{"name":1818,"slug":1819,"type":15},"Operations","operations","2026-07-17T05:28:56.913999",{"slug":1822,"name":1822,"fn":1823,"description":1824,"org":1825,"tags":1826,"stars":20,"repoUrl":21,"updatedAt":1834},"cudaq-guide","develop quantum applications with CUDA-Q","CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1827,1828,1831],{"name":9,"slug":8,"type":15},{"name":1829,"slug":1830,"type":15},"Quantum Computing","quantum-computing",{"name":1832,"slug":1833,"type":15},"Simulation","simulation","2026-07-14T05:26:58.898253",305]