Quantum Federated Digital Twin Framework with Explainable Multi-Agent Intelligence for Autonomous Engineering Management
Nilesh Vasant Ingale, Mahesh Dhande, Pankaj Deshmukh
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 simulated multi-site digital-twin testbed spanning predictive maintenance, dynamic scheduling, and energy-aware operation, and is benchmarked against classical federated averaging with single- and multi-agent deep reinforcement learning, and against quantum federated learning without multi-agent coordination. Simulation results indicate that QFDT-XMAI improves normalised policy return by 6 to 15 percentage points over the strongest baseline, reduces communication rounds required for convergence by approximately 22 percent, and raises the proposed Explainability Fidelity Index from 0.40–0.55 to 0.90 while maintaining comparable decision latency. The findings suggest that jointly engineering quantum-assisted federation, multi-agent coordination, and explainability, rather than treating them as separate add-ons, yields measurable gains for autonomous engineering management under data-locality and auditability constraints
•Sep 11, 2026•0 citations