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
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
Autonomous agents are being used in more complex and dynamic environments where efficient learning, adaptive decision-making, and scalable optimization are required. Although classical machine learning (ML) and reinforcement methods have greatly contributed to agent intelligence, they are usually restricted by constraints of computational complexity, scaling, and convergence to high-dimensional decision space. The current developments in quantum computing have created new possibilities to solve these problems using quantum ML (QML). In this chapter, the authors discuss the implementation of QML in collaboration with autonomous agents in the larger paradigm of agentic artificial intelligence (AI) and quantum AI agents. It offers a hybrid classical–quantum architectural model where quantum learning modules improve perception, policy learning, and decision optimization, and do not conflict with classical agent pipelines. The chapter analyzes application cases in autonomous robotics, intelligent transportation systems, multi-agent coordination, smart energy management, as well as communication networks, and the chapter shows the potential benefits of quantum-enhanced learning in these areas. The issue of quantum hardware neediness, data encoding, scaling, hybrid integration, and real-life application are critically evaluated and unanswered. In summary, the chapter is a well-organized approach to the purpose of QML in the development of the next-generation autonomous agent and an insight into creating practical, scalable, and intelligent quantum AI agents.
The rapid pace of development in healthcare artificial intelligence requires architectures that transcend predictive analytics to better understand causality and generate simulations of patient trajectories. This study proposes the Quantum Edge Federated Graph Transformer (QFGT), a hybrid quantum-inspired approach aimed at empowering causal, generative, and privacy-preserving digital twin healthcare. The model incorporates quantum kernel attention for complex-amplitude feature embedding, graph transformer reasoning mechanism for relational inference over multimodal clinical entities, and federated optimization for secure multi-institutional learning. Quantum-inspired kernel mappings in Hilbert space encode entangled dependencies between laboratory, imaging, genomic, and clinical text data, while a causal regularization layer constrains learned representations to adhere to interpretable cause-and-effect relations learned from structural causal models. The generative digital-twin module uses a diffusion-based latent simulator that predicts personalized trajectories of the disease, and it supports what-if counterfactual interventions. Federated deployment at the edge healthcare nodes enables model training with data decentralization and strict compliance with HIPAA and GDPR privacy regulations. Experimental work on multimodal clinical data shows an accuracy of 98.1% with a mean early-detection window of 7.9 months and F1-scores >0.98 for all diseases, in addition to an increase in minority-cohort recall of 6.5% via equitable quantum-kernel feature sharing. The proposed QFGT framework opens up a new direction for the quantum-inspired, federated, and causally explainable digital twin systems, which lead to trustworthy, proactive, and personal healthcare intelligence at the quantum edge.
The increasing constraints of climate change and data privacy necessitate high-efficiency, sustainable agriculture, which is causing a shift in the paradigm of Agro-informatics. Most classical agricultural models fail to capture genotype, soil chemistry, and climate dynamics connections. Latent interactions, essential to intelligent agricultural treatments and explainability, are lost in many data processing pipelines that use linear dimensionality reduction or black-box learning. This paper presents a quantum computing architecture for a revolutionary agricultural application, utilizing quantum encoding, topological learning, reinforcement optimization, federated intelligence, and explainability to highlight the importance of this vital field. In Quantum Variational Crop-Soil Entanglement Encoding, crop-soil interaction datasets are encoded into quantum state vectors using variational circuits, preserving high-order entanglement properties (fidelity > 0.96, entropy ~ 0.9). Quantum-guided agri-topological dynamics mapping transforms encoded states into permanent topological maps using a hybrid quantum-classical Topological Data Analysis to track climate-induced agri-system dynamics (r = 0.84 with the yield index). Field-level decisions using Quantum Reinforcement Learning for Precision Intervention policy mappings to relate topological states to interventions produce 16.2% normalized yield. Quantum Federated Learning for Distributed Farm Intelligence uses privacy-preserving, encrypted quantum policy gradients to enable learning across farms in varied locations, lowering communication by 42% and improving accuracy by 9.3%. Quantum Explainability through Entropic Intervention Attribution generates causal graphs of yield drivers with 89% confidence intervals using entropy-based attributions. This integrated framework enhances the knowledge preservation, policy accuracy, expandability, and trust of agricultural Artificial Intelligence systems, enabling quantum-accelerated, information-based, future-ready farming decision support systems.
Abstract: Autonomous cyber-physical networks increasingly combine distributed sensing, edge intelligence, networked control, and safety-critical actuation, yet their reliability is constrained by correlated cyber faults, communication loss, model drift, hardware degradation, and the fragility of emerging quantum resources. This paper develops a fault-tolerant distributed quantum-intelligence architecture that couples photonic circuit emulation, quantum error-aware computation, AI-augmented control, distributed consensus, and deterministic safety fallback. The purpose is to formalize how heterogeneous quantum, photonic, AI, and control subsystems can jointly preserve mission-level stability when individual components fail or become uncertain. The study adopts a model-based systems methodology, synthesizes the supplied literature on fault-tolerant photonic quantum computing, distributed autonomous control, cyber-physical resilience, AI-native edge systems, and quantum-assisted optimization, and formulates a multi-layer state-space and reliability model. A composite Fault-Tolerant Quantum Intelligence Resilience Index (FTQIRI) integrates quantum reliability, photonic emulation fidelity, AI robustness, network resilience, closed-loop stability, and cyber assurance. An illustrative 0–10 numerical model yields an integrated score of 8.49 for the proposed architecture versus 6.34 for a conventional hybrid baseline, indicating the expected value of cross-layer redundancy and adaptive orchestration rather than empirical superiority. The paper further specifies an invention-oriented architecture, independent system and method claim drafts, and dependent claim concepts around digital-twin emulation, syndrome-informed control, consensus-based failover, cryptographic verification, and safety envelopes. The principal contribution is a unified mathematical and engineering framework that treats quantum fault tolerance and cyber-physical fault tolerance as coupled control problems, thereby connecting photonic quantum hardware, distributed intelligence, and autonomous network safety within one verifiable decision loop. Keywords: fault-tolerant quantum computing; distributed quantum intelligence; photonic circuit emulation; autonomous cyber-physical systems; AI-augmented control; quantum error correction; resilient edge intelligence; distributed consensus; safe reinforcement learning; digital twin; quantum–classical co-design; cyber resilience; multi-agent control; reliability optimization; verifiable AI
This chapter develops an integrated framework for quantum-enhanced federated digital twins in Internet of Medical Things (IoMT)-based smart healthcare systems. It explains how continuous sensing, edge intelligence, federated learning, digital twin modeling, and near-term hybrid quantum-classical machine learning can be combined to support privacy-preserving predictive and personalized medicine. The chapter argues that the value of quantum-enhanced healthcare should not be framed as immediate quantum superiority, but as a disciplined pathway for testing quantum kernels, variational circuits, quantum-inspired optimization, and hybrid classifiers against strong classical baselines. It presents a layered architecture, clinical use cases, security safeguards, implementation challenges, and research directions. The chapter provides a clinically grounded and ethically cautious roadmap for researchers, healthcare AI developers, hospital leaders, and policymakers seeking trustworthy intelligent healthcare without centralizing sensitive patient data.
Abstract: This study investigates an explainable quantum-logical multi-agent system (QL-MAS) for autonomous scientific discovery in high-dimensional research landscapes. The central methodological problem is that modern discovery settings combine large numbers of variables, heterogeneous evidence types, and partially conflicting reasoning signals, yet conventional single-model pipelines often compress this complexity into opaque rankings. The proposed framework addresses this limitation by treating candidate scientific hypotheses as quantum-logical superpositions of evidence generated by three complementary agents: an evidence agent based on mutual information, a structure agent based on principal-component loadings, and a rule agent based on interpretable decision structures. The resulting architecture does not claim physical quantum computation; rather, it uses quantum-logical amplitude composition as a mathematically disciplined way to represent concurrent support, interference, and consensus across agents. Empirical evaluation is conducted on three real public datasets that approximate heterogeneous scientific discovery landscapes: the Breast Cancer Wisconsin (Diagnostic) dataset (569 observations, 30 variables), the Wine Recognition dataset (178 observations, 13 variables), and the Diabetes dataset used in the least-angle-regression literature (442 observations, 10 baseline variables). These corpora were selected because they are non-synthetic, scientifically grounded, and sufficiently high-dimensional to test feature prioritization, interpretability, and cross-domain transfer. Across the three datasets, the proposed system identifies compact sets of high-salience hypotheses while preserving strong downstream predictive utility. The top-five features selected by QL-MAS attain cross-validated scores of 0.965 on breast cancer classification, 0.955 on wine classification, and 0.465 R² on diabetes progression, remaining close to full-feature baselines while offering substantially more interpretable evidence paths. The results indicate that explainable multi-agent discovery is strongest when high predictive relevance and rule-based consistency converge, and weakest when latent structure is diffuse or poorly aligned with supervised objectives. The analysis therefore supports the conclusion that autonomous discovery systems in research environments should not be assessed solely by predictive accuracy, but by the transparency of their evidential decomposition and by the stability of agreement across reasoning agents. The paper contributes a theoretically coherent framework, a real-data evaluation protocol, and a discussion of limitations, governance, and future directions for autonomous scientific reasoning systems. Keywords: autonomous scientific discovery; explainable AI; multi-agent systems; quantum logic; high-dimensional data; interpretable machine learning; research landscapes