Quantum Federated Digital Twin Framework with Explainable Multi-Agent Intelligence for Autonomous Engineering Management
Abstract
Abstract Autonomous engineering management increasingly depends on distributed digital twins that must learn, coordinate, and justify decisions across geographically separated industrial sites without exposing proprietary operating data. Existing solutions address these needs only in isolation: federated learning preserves data locality but offers no formal guarantee against gradient-based information leakage; multi-agent reinforcement learning coordinates distributed controllers but rarely explains its own decisions; and digital twins provide high-fidelity virtual representations but are seldom coupled to a decision layer that can act autonomously while remaining auditable by human engineers. This paper proposes the Quantum Federated Digital Twin framework with eXplainable Multi-Agent Intelligence (QFDT-XMAI), a five-layer architecture that couples asset-level digital twins to a quantum-enhanced federated learning layer, a graph-attention multi-agent decision layer, and a dedicated explainability layer. The core methodological contribution is a quantum-fidelity-weighted aggregation rule (QFA-FW) that combines classical federated averaging with a quantum state-overlap weighting term computed from variational quantum circuits trained locally at each site, together with an explainable multi-agent proximal policy optimisation procedure (XMAPPO) that couples agent coordination with Shapley-based attribution and counterfactual validity scoring. The framework is evaluated on a…
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