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{
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{
            
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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Yuxuan Du",
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  "author": "Yuxuan Du",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [],
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  "citation": [],
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.1038/s41534-022-00570-y",
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  "defined_in": "https://doi.org/10.1038/s41534-022-00570-y",
            
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  "doi": "10.57702/92dbd79m",
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  "doi": "10.57702/92dbd79m",
            
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  "doi_date_published": null,
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  "doi_date_published": "2025-01-03",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "doi_status": false,
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  "doi_status": true,
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Tao Huang",
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      "extra_author": "Tao Huang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Shan You",
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      "extra_author": "Shan You",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Min-Hsiu Hsieh",
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      "extra_author": "Min-Hsiu Hsieh",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Dacheng Tao",
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      "extra_author": "Dacheng Tao",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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    }
            
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  ],
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  ],
            
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  "groups": [
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  "groups": [
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Machine Learning",
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      "display_name": "Machine Learning",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "machine-learning",
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      "name": "machine-learning",
            
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      "title": "Machine Learning"
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      "title": "Machine Learning"
            
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    },
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    },
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Quantum Computing",
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      "display_name": "Quantum Computing",
            
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      "id": "37f69467-8e9a-4c20-8596-191ee5661e68",
            
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      "image_display_url": "",
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      "image_display_url": "",
            
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      "name": "quantum-computing",
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      "name": "quantum-computing",
            
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      "title": "Quantum Computing"
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      "title": "Quantum Computing"
            
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    }
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    }
            
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  ],
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  ],
            
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  "id": "6c4c8617-5e66-49fd-aeee-474a76405b6e",
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  "id": "6c4c8617-5e66-49fd-aeee-474a76405b6e",
            
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  "isopen": false,
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  "isopen": false,
            
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  "landing_page": "",
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  "license_title": null,
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  "license_title": null,
            
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  "link_orkg": "",
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  "link_orkg": "",
            
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  "metadata_created": "2025-01-03T00:21:38.385582",
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  "metadata_created": "2025-01-03T00:21:38.385582",
            
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  "metadata_modified": "2025-01-03T00:21:38.385587",
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  "metadata_modified": "2025-01-03T00:21:38.933642",
            
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  "name": "synthetic-dataset-d",
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  "name": "synthetic-dataset-d",
            
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  "notes": "The synthetic dataset D used in the main text is 
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  "notes": "The synthetic dataset D used in the main text is 
            
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constructed by generating a set of data points {x(i)} with x(i) \u2208 
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constructed by generating a set of data points {x(i)} with x(i) \u2208 
            
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R3. The optimal circuit is Ux = RY(x1) \u2297 RY(x2) \u2297 RY(x3). 
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R3. The optimal circuit is Ux = RY(x1) \u2297 RY(x2) \u2297 RY(x3). 
            
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The strategy to label x(i) is as follows. Let \u03a0 = I4 \u2297 
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The strategy to label x(i) is as follows. Let \u03a0 = I4 \u2297 
            
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|0(cid:105) (cid:104)0| be the measurement operator. The data point 
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|0(cid:105) (cid:104)0| be the measurement operator. The data point 
            
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x(i) is labeled as y(i) = 1 if (cid:104)000|U \u2020x(i)U 
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x(i) is labeled as y(i) = 1 if (cid:104)000|U \u2020x(i)U 
            
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\u2217(\u03b8\u2217)\u2020\u03a0U \u2217(\u03b8\u2217)Ux(i) 
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\u2217(\u03b8\u2217)\u2020\u03a0U \u2217(\u03b8\u2217)Ux(i) 
            
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|000(cid:105) \u2265 0.75. The label of x(i) is assigned as y(i) = 0 
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|000(cid:105) \u2265 0.75. The label of x(i) is assigned as y(i) = 0 
            
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if (cid:104)000|U \u2020x(i)U \u2217(\u03b8\u2217)\u2020\u03a0U 
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if (cid:104)000|U \u2020x(i)U \u2217(\u03b8\u2217)\u2020\u03a0U 
            
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\u2217(\u03b8\u2217)Ux(i) |000(cid:105) \u2264 0.25.",
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\u2217(\u03b8\u2217)Ux(i) |000(cid:105) \u2264 0.25.",
            
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  "num_tags": 3,
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  "organization": {
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  "organization": {
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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    "created": "2024-11-25T12:11:38.292601",
            
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    "image_url": "",
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    "state": "active",
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    "title": "No Organization",
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    "type": "organization"
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    "type": "organization"
            
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  },
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  "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559",
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  "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559",
            
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  "private": false,
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  "private": false,
            
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  "relationships_as_object": [],
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  "relationships_as_subject": [],
            
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  "resources": [],
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  "resources": [
            
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    {
            
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      "cache_last_updated": null,
            
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      "created": "2025-01-03T00:16:32",
            
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      "data": [
            
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        "dcterms:title",
            
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        "dcterms:accessRights",
            
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      ],
            
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      "description": "The json representation of the dataset with its 
            
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distributions based on DCAT.",
            
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      "format": "JSON",
            
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      "hash": "",
            
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      "id": "48da3d9d-cd1b-4445-b973-289d654a37a7",
            
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      "last_modified": "2025-01-03T00:21:38.925965",
            
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      "mimetype": "application/json",
            
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      "name": "Original Metadata",
            
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      "url": 
            
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resource/48da3d9d-cd1b-4445-b973-289d654a37a7/download/metadata.json",
            
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      "url_type": "upload"
            
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    }
            
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  ],
            
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  "state": "active",
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  "state": "active",
            
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  "tags": [
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  "tags": [
            
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    {
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    {
            
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      "display_name": "Machine learning",
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      "display_name": "Machine learning",
            
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      "id": "7c8050e7-2e86-4a96-aebe-6c582621940d",
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      "id": "7c8050e7-2e86-4a96-aebe-6c582621940d",
            
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      "name": "Machine learning",
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      "name": "Machine learning",
            
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    },
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    {
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    {
            
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      "display_name": "Quantum computing",
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      "display_name": "Quantum computing",
            
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      "id": "d10aa058-c6c5-49d6-901d-1460eca5126e",
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      "id": "d10aa058-c6c5-49d6-901d-1460eca5126e",
            
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      "name": "Quantum computing",
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      "name": "Quantum computing",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Synthetic dataset",
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      "display_name": "Synthetic dataset",
            
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      "id": "23852e66-6846-4906-8126-937b64f02ee9",
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      "id": "23852e66-6846-4906-8126-937b64f02ee9",
            
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      "name": "Synthetic dataset",
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      "name": "Synthetic dataset",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Synthetic dataset D",
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  "title": "Synthetic dataset D",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}