Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917197969883136 |
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| author | Shaik, Thanveer Tao, Xiaohui Xie, Haoran |
| author_facet | Shaik, Thanveer Tao, Xiaohui Xie, Haoran |
| contents | Quantum Machine Unlearning has emerged as a foundational challenge at the intersection of quantum information theory privacypreserving computation and trustworthy artificial intelligence This paper advances QMU by establishing a formal framework that unifies physical constraints algorithmic mechanisms and ethical governance within a verifiable paradigm We define forgetting as a contraction of distinguishability between pre and postunlearning models under completely positive trace-preserving dynamics grounding data removal in the physics of quantum irreversibility Building on this foundation we present a fiveaxis taxonomy spanning scope guarantees mechanisms system context and hardware realization linking theoretical constructs to implementable strategies Within this structure we incorporate influence and quantum Fisher information weighted updates parameter reinitialization and kernel alignment as practical mechanisms compatible with noisy intermediatescale quantum NISQ devices The framework extends naturally to federated and privacyaware settings via quantum differential privacy homomorphic encryption and verifiable delegation enabling scalable auditable deletion across distributed quantum systems Beyond technical design we outline a forwardlooking research roadmap emphasizing formal proofs of forgetting scalable and secure architectures postunlearning interpretability and ethically auditable governance Together these contributions elevate QMU from a conceptual notion to a rigorously defined and ethically aligned discipline bridging physical feasibility algorithmic verifiability and societal accountability in the emerging era of quantum intelligence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00406 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy Shaik, Thanveer Tao, Xiaohui Xie, Haoran Quantum Physics Artificial Intelligence Quantum Machine Unlearning has emerged as a foundational challenge at the intersection of quantum information theory privacypreserving computation and trustworthy artificial intelligence This paper advances QMU by establishing a formal framework that unifies physical constraints algorithmic mechanisms and ethical governance within a verifiable paradigm We define forgetting as a contraction of distinguishability between pre and postunlearning models under completely positive trace-preserving dynamics grounding data removal in the physics of quantum irreversibility Building on this foundation we present a fiveaxis taxonomy spanning scope guarantees mechanisms system context and hardware realization linking theoretical constructs to implementable strategies Within this structure we incorporate influence and quantum Fisher information weighted updates parameter reinitialization and kernel alignment as practical mechanisms compatible with noisy intermediatescale quantum NISQ devices The framework extends naturally to federated and privacyaware settings via quantum differential privacy homomorphic encryption and verifiable delegation enabling scalable auditable deletion across distributed quantum systems Beyond technical design we outline a forwardlooking research roadmap emphasizing formal proofs of forgetting scalable and secure architectures postunlearning interpretability and ethically auditable governance Together these contributions elevate QMU from a conceptual notion to a rigorously defined and ethically aligned discipline bridging physical feasibility algorithmic verifiability and societal accountability in the emerging era of quantum intelligence. |
| title | Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy |
| topic | Quantum Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.00406 |