{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MPKNPQ2POGE5JIT6GITBYCF6T5","short_pith_number":"pith:MPKNPQ2P","schema_version":"1.0","canonical_sha256":"63d4d7c34f7189d4a27e32261c08be9f602aa62b86f5b78a98764fb8f2ab18fb","source":{"kind":"arxiv","id":"2406.17248","version":3},"attestation_state":"computed","paper":{"title":"MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Abolfazl Bayat, Chufan Lyu, Fan Yu, Guilu Long, Haiying Zhao, Jiale Liu, Jiale Lu, Jialiang Tang, Jiangyu Cui, Junyuan Zhou, Kang Yang, Man-Hong Yung, Maolin Luo, Mosharev Pavel, Pan Zhang, Qiang Zheng, Qingguo Zeng, Qingyu Li, Re-Bing Wu, Runhong He, Runqiu Shu, Ruoqian Xu, Shijie Pan, ShiJie Wei, Shu Xu, Wei Cui, Wuxin Liu, Xiaoting Wang, Xiaowei Li, Xi Cao, Xusheng Xu, Xu Zhou, Yanling Lin, Yikang Zhu, Zhaofeng Su, Zhendong Li, Zidong Cui, Zizhu Wang, Zuoheng Zou","submitted_at":"2024-06-25T03:28:40Z","abstract_excerpt":"We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when ex"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.17248","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2024-06-25T03:28:40Z","cross_cats_sorted":[],"title_canon_sha256":"5cc7de61c6e9f5858084f6670a92ad7f4ed446877e2fac9750abafbfec1d7c36","abstract_canon_sha256":"446de9b4c60e1113b193aab3faa67a39e2c0e2b8566c4fc87d8a7999a4fa8464"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:04.966947Z","signature_b64":"eI2LnuCMo+Cpd8g4W+LQDVXk5WL5t6o7lBGis/50fsRI9wCyBdnhO6SKfUEFcNRG27XeX2/PLvUtzuIkZ35NAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63d4d7c34f7189d4a27e32261c08be9f602aa62b86f5b78a98764fb8f2ab18fb","last_reissued_at":"2026-07-05T08:42:04.966513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:04.966513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Abolfazl Bayat, Chufan Lyu, Fan Yu, Guilu Long, Haiying Zhao, Jiale Liu, Jiale Lu, Jialiang Tang, Jiangyu Cui, Junyuan Zhou, Kang Yang, Man-Hong Yung, Maolin Luo, Mosharev Pavel, Pan Zhang, Qiang Zheng, Qingguo Zeng, Qingyu Li, Re-Bing Wu, Runhong He, Runqiu Shu, Ruoqian Xu, Shijie Pan, ShiJie Wei, Shu Xu, Wei Cui, Wuxin Liu, Xiaoting Wang, Xiaowei Li, Xi Cao, Xusheng Xu, Xu Zhou, Yanling Lin, Yikang Zhu, Zhaofeng Su, Zhendong Li, Zidong Cui, Zizhu Wang, Zuoheng Zou","submitted_at":"2024-06-25T03:28:40Z","abstract_excerpt":"We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-source deep learning training/inference framework, MindSpore Quantum exhibits exceptional efficiency in the design and training of variational quantum algorithms on both CPU and GPU platforms, delivering remarkable performance. Furthermore, this framework places a strong emphasis on enhancing the operational efficiency of quantum algorithms when ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.17248","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.17248/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.17248","created_at":"2026-07-05T08:42:04.966570+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.17248v3","created_at":"2026-07-05T08:42:04.966570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.17248","created_at":"2026-07-05T08:42:04.966570+00:00"},{"alias_kind":"pith_short_12","alias_value":"MPKNPQ2POGE5","created_at":"2026-07-05T08:42:04.966570+00:00"},{"alias_kind":"pith_short_16","alias_value":"MPKNPQ2POGE5JIT6","created_at":"2026-07-05T08:42:04.966570+00:00"},{"alias_kind":"pith_short_8","alias_value":"MPKNPQ2P","created_at":"2026-07-05T08:42:04.966570+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23300","citing_title":"Local-Observable-Guided Generative Quantum Circuits for Degenerate Ground Spaces","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2502.15375","citing_title":"Digitized Counter-Diabatic Quantum Optimization for Bin Packing Problem","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2509.07460","citing_title":"Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2512.18995","citing_title":"DeepQuantum: A PyTorch-based Software Platform for Quantum Machine Learning and Photonic Quantum Computing","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2601.11942","citing_title":"Geometric Preconditioning and Curriculum Optimization for Trainable Variational Quantum Regression","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2603.25762","citing_title":"QHap: Quantum-Inspired Haplotype Phasing","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2603.28413","citing_title":"Resource-efficient quantum approximate optimization algorithm via Bayesian optimization and maximum-probability evaluation","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5","json":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5.json","graph_json":"https://pith.science/api/pith-number/MPKNPQ2POGE5JIT6GITBYCF6T5/graph.json","events_json":"https://pith.science/api/pith-number/MPKNPQ2POGE5JIT6GITBYCF6T5/events.json","paper":"https://pith.science/paper/MPKNPQ2P"},"agent_actions":{"view_html":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5","download_json":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5.json","view_paper":"https://pith.science/paper/MPKNPQ2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.17248&json=true","fetch_graph":"https://pith.science/api/pith-number/MPKNPQ2POGE5JIT6GITBYCF6T5/graph.json","fetch_events":"https://pith.science/api/pith-number/MPKNPQ2POGE5JIT6GITBYCF6T5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5/action/storage_attestation","attest_author":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5/action/author_attestation","sign_citation":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5/action/citation_signature","submit_replication":"https://pith.science/pith/MPKNPQ2POGE5JIT6GITBYCF6T5/action/replication_record"}},"created_at":"2026-07-05T08:42:04.966570+00:00","updated_at":"2026-07-05T08:42:04.966570+00:00"}