{"authors":[],"components":[{"id":"root","name":"root","payload":{"cid":"bafybeia6xcsuueksxqmkfkbohqxxx7zsl7mbe5utjguue4fzy5ruhkmioe","path":"root"},"type":{".pdf":"pdf"}},{"id":"5abc03b4-0904-4ded-a8b1-c0c08e423320","name":"QuantumCloudStorageAlgorithm20.0.pdf","type":"pdf","payload":{"cid":"bafybeidu3wueez5ytvsnazedxsrgooa37vdejhyt2yxwaxmrbdav3jfufq","path":"root/QuantumCloudStorageAlgorithm20.0.pdf","title":"Manuscript"},"starred":true,"subtype":"manuscript"}],"defaultLicense":"CC BY","keywords":["computer science","semantics","projection","fidelity","distributed computing","theoretical computer science","computer architecture","programming language","algorithm","telecommunications"],"researchFields":["Cloud Computing and Big Data Technologies"],"title":"Quantum Cloud Storage Algorithm 20.0（QMC 20.0）","version":"desci-nodes-0.2.0","references":[],"description":"This paper presents a revolutionary memory system architecture-Quantum Memory Cloud 20.0 (QMC20). It transcends traditional linear data retrieval models and constructs a dynamical framework based on bidirectional projection between micro-representations and macro-semantics. Compared with the basic logic of version 1.0, QMC20 introduces entangled semantic caching technology, which achieves sub-second retrieval in high-dimensional vector spaces by simulating local condensation effects in physical systems. The system' s built-in progressive reconstruction pipelines ensure the fidelity of information during cross-scale mapping. In addition, QMC20 is the first to implement system-level detox and immune quarantine mechanisms at the storage layer, providing a stable, secure, and self-healing long-term memory substrate for artificial life forms and cognitive agents."}