Quantum Machine Unlearning: Foundations, Mechanisms, and Taxonomy

2025-11-01

AI Summary

Quantum machine unlearning lacks a unified framework integrating physical constraints, algorithmic mechanisms, and ethical governance, resulting in unverifiable deletion standards and a fundamental tension between irreversibility and practicality in quantum settings. Method: We propose the first five-axis taxonomy to rigorously define model unlearning criteria grounded in quantum irreversibility. Our algorithm synergistically combines quantum Fisher information–weighted parameter updates, parameter reinitialization, and kernel alignment. It is augmented with quantum differential privacy, quantum homomorphic encryption, and verifiable delegation. Contribution/Results: We present the first scalable, auditable, and verifiable quantum unlearning framework compatible with NISQ devices. The solution enables trustworthy federated learning and privacy-sensitive applications in distributed quantum systems. By bridging quantum information theory, machine learning, and privacy-preserving computation, our work establishes both theoretical foundations and engineering pathways toward quantum-trustworthy AI.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

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.

Problem

• Establishing a formal framework for quantum machine unlearning with physical constraints
• Defining forgetting as quantum distinguishability contraction under trace-preserving dynamics
• Developing practical mechanisms compatible with noisy quantum computing devices

Innovation

• Framework unifying physical constraints and ethical governance
• Taxonomy linking theory to implementable NISQ strategies
• Mechanisms enabling scalable auditable deletion in quantum systems