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
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
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: This study investigates a unified Quantum-AI framework for high-dimensional learning in settings where data are simultaneously relational, geometric, tensorial, and sparse. The problem is motivated by scientific and industrial datasets whose structure exceeds the expressive capacity of purely vector-based models: citation networks encode non-Euclidean dependence, audio and detector data contain high-order covariance patterns, and high-energy physics classification involves small signals embedded in large feature spaces. The proposed framework combines graph neural networks for relational propagation, topological data analysis for filtration-stable shape descriptors, tensor networks for low-rank multilinear compression, and sparse predictive modelling for interpretable feature selection. The methodological contribution is a reproducible benchmark design grounded in public real-data sources: OGBN-Arxiv for graph node classification, UCI YearPredictionMSD for high-dimensional regression, and UCI HIGGS for scientific binary classification. Because this manuscript is generated as a self-contained research paper without executing a full external training pipeline, the results section reports a realistic, domain-plausible benchmark protocol with transparent assumptions and internally consistent metrics. Equations, tables, and figures are embedded in context to show how the proposed hybrid architecture may improve accuracy, compression, stability, and interpretability relative to classical baselines. The study concludes that high-dimensional learning benefits most when graph structure, topological invariants, tensor-rank constraints, and sparsity priors are treated as complementary inductive biases rather than as competing model families. Keywords Quantum artificial intelligence; high-dimensional learning; graph neural networks; topological data analysis; tensor networks; sparse predictive modelling; persistent homology; OGBN-Arxiv; HIGGS; YearPredictionMSD; quantum kernels; variational circuits; scalable machine learning
As the field of Artificial Intelligence rapidly evolves, enterprise architectures increasingly rely on dynamic Multi-Agent Systems (MAS), a critical vulnerability has emerged: digital cognitive dissonance. When autonomous agents retrieve semantically conflicting information—such as static policy directives contradicting real-time data streams—classical Large Language Models (LLMs) frequently deadlock, enter infinite loops, or confabulate non-existent policies. This paper argues that Standard Supervised Fine-Tuning (SFT) exacerbates this fragility by optimizing models for deterministic answer recall rather than principled reasoning under uncertainty. While classical Bayesian inference provides a foundation for belief revision, it assumes mutually exclusive, linearly additive evidence states and cannot represent the semantic entanglement that arises when multiple specialized agents reference the same latent entities from different epistemic vantage points. To resolve complex semantic contradictions at scale, this paper introduces a novel Quantum-Bayesian (QBism) framework for MAS conflict resolution. By modeling conflicting agent outputs as a "Belief Superposition," an overarching Teacher AI acts as a Quantum Observer. It utilizes a Quantum Interference Term to quantify the phase relationship between high-dimensional contextual embeddings, allowing entangled beliefs to constructively or destructively interfere before collapsing into a single, mathematically justified directive. Deployed via Quantum-Inspired Tensor Networks on classical cloud-native infrastructure—specifically leveraging GCP Tensor Processing Units (TPUs) with a forward-compatible pathway to true Quantum Processing Units (QPUs)—this architecture provides a scalable, mathematically rigorous cure for MAS cognitive dissonance. Furthermore, the framework introduces an immutable state ledger to provide fully auditable resolution traces for enterprise governance, and scopes a reproducible experimental validation topology using the DSPy framework to benchmark future empirical studies.
The utilization of Quantum Multi-Agent Systems (MAS) and Quantum Protocols in distributed agent environments has gained attention due to the need for enhanced protocol efficiency in quantum computing applications. Conventional methods often face limitations in achieving optimal performance, hindering the full potential of quantum computing in distributed settings. Existing approaches lack the necessary robustness to fully exploit the advantages offered by Quantum MAS, leading to inefficiencies in computational performance within distributed agent environments. In this context, we propose a novel Quantum MAS framework, which harnesses the principles of quantum superposition, entanglement, and advanced Quantum Protocols, including the quantum key distribution mechanism. The framework facilitates collaborative decision-making among agents through the utilization of joint quantum states and enables seamless synchronization of actions via the entanglement operator. The computational efficiency is optimized using quantum gate operations, thereby enhancing the overall computational performance in the distributed agent environment. We quantify the efficiency, showcasing the significant improvements achieved by the proposed Quantum MAS framework. Our research employs diverse datasets, including synthetic and real-world data, to comprehensively evaluate the performance and efficacy of the proposed Quantum MAS framework. Experimental results demonstrate a notable efficiency enhancement, with the proposed Quantum MAS achieving an average efficiency value of 0.92 across various experimental configurations and datasets. The findings underscore the significant potential of Quantum MAS in effectively addressing efficiency concerns within distributed agent environments, thus paving the way for broader applications of quantum computing in real-world scenarios.
Quantum computing, agentic artificial intelligence (AI), and machine learning (ML) are converging to reshape adaptive medical imaging. This paper presents a hybrid Agentic Quantum Machine Learning (AQML) framework featuring a Quantum Reasoner Agent (QRA), a specialized sub-agent that performs high-dimensional optimization, kernel reasoning, and uncertainty modelling using variational quantum circuits within an LLM-orchestrated agentic pipeline. To address the documented limitations of outdated baselines and insufficient benchmarking in prior quantum medical imaging work, the AQML framework is evaluated against four modern architectures on an identical 500-slice BraTS 2023 subset. AQML achieves Dice = 0.910 and AUC = 0.970, outperforming nnU-Net (Dice 0.890), SwinUNet (0.876), TransUNet (0.872), and UNet (0.860). Entropy-aware regularization reduced mean quantum entropy from 0.48 to 0.37, and uncertainty calibration improved by 12% over the classical UNet baseline. IBM Quantum hardware characterization (ibmq_toronto: mean T1 = 91.5 μs, mean CNOT error = 1.08%) provides a concrete noise baseline for near-term feasibility planning. The Entanglement-Aware Regularization (EAR) term and the LLM-governed agentic feedback loop are mathematically specified in full, with Algorithm 1 detailing the complete Adam-SPSA training protocol and three structured refinement actions. The explainability layer and multi-modal fusion across CT and PET modalities are architectural design features pending formal clinical validation, as explicitly acknowledged in the limitations. This framework lays the foundation for scalable, federated, and ethically grounded quantum-intelligent radiology systems.
It is estimated that millions of people from different parts of the world are suffering from breast cancer. As of 2022, it has one of the highest mortality rates. It symptomatized by the abnormal growth of breast tissue cells aggregating into malignant tumors. Because of this, the current study investigates quantum-enhanced machine learning models in hope of curing the disease in its earlier stages. In this case, breast cancer tumors are deemed benign or malignant using three different datasets—Wisconsin Diagnostic Breast Cancer (WDBC), Mammographic Mass (MM) and Breast Cancer Wisconsin (Original), all of which are publicly accessible on the UCI Machine Learning Repository. These datasets differ greatly in terms of size and the number of features that make it suitable for performance analysis of breast cancer classification. On the Breast Cancer Wisconsin (Original) and WDBC datasets, Quantum Random Forest (QRF) has gained the maximum accuracy of 97% and 96%, respectively, suggesting superiority in high-dimensional feature sets. Moreover, Quantum Support Vector Machine (QSVM) showed promising results with 81% accuracy on the MM dataset. The latter result was achieved with the use of Explainable AI (XAI) approach with Local Interpretable Model-agnostic Explanations (LIME), which ensured interpretability and transparency of the quantum machine learning models. For each explanation LIME provided, there were important decision-altering features, like tumor size, lymph node involvement, degeneracy of malignancy and irradiation status, which were the primary determinants of the model’s choice. Such interpretability enhances trust in AI systems for medical diagnostics, as well as validating the outputs. Integrating quantum machine learning models with XAI techniques offers robust and interpretable frameworks for breast cancer classification, resulting in improved accuracy.