Farseer: Research on an Integrated Quantum Sensing Foundation for ISR A Next-Generation Intelligence Collection System Integrating Diverse Quantum Platforms, Heterogeneous Sensor Fusion, Distributed Quantum Networks, Hierarchical Situational Awareness, and the Quantum Thinking Circuit OS ASI
Farseer is a research proposal for an integrated quantum sensing foundation for ISR, redefining intelligence, surveillance, and reconnaissance from single-sensor detection into a multilayer information collection architecture. It combines diverse quantum platforms, classical sensors, distributed sensor networks, redundant communications, edge AI, brain-type processing, alternative power and cooling, self-diagnosis, self-repair, and hierarchical situational awareness. The system evaluates performance through Q_ISR and is controlled by the Quantum Thinking Circuit OS ASI, which selects sensors, fuses data, interprets noise and failures, suppresses ambiguous judgments, and branches into safe operational modes. The study presents Farseer as a high-reliability, auditable, adaptive, and modular next-generation ISR infrastructure.
Farseer is a research proposal for an integrated quantum sensing foundation for ISR, redefining intelligence, surveillance, and reconnaissance from single-sensor detection into a multilayer information collection architecture. It combines diverse quantum platforms, classical sensors, distributed sensor networks, redundant communications, edge AI, brain-type processing, alternative power and cooling, self-diagnosis, self-repair, and hierarchical situational awareness. The system evaluates performance through Q_ISR and is controlled by the Quantum Thinking Circuit OS ASI, which selects sensors, fuses data, interprets noise and failures, suppresses ambiguous judgments, and branches into safe operational modes. The study presents Farseer as a high-reliability, auditable, adaptive, and modular next-generation ISR infrastructure.
Integrated Study on a Deterministic Autonomous Computing Architecture Based on Quantum Thought Circuit OS ASI. Physical-Digital Twin Integration, Energy-Circulating Computation, High-Speed Communication, Dynamic Regulatory Control, Distributed Infrastructure, and Hardware-Rooted Trus. A Six-Part Structure Part I: Integrated Study on Heterogeneous Self-Optimizing Computing Architecture with Physical Digital Twin, Thermodynamic Energy Recycling, and Autonomous Distributed Control Based on Quantum Thought Circuit OS ASI Part II: Integrated Study on Next-Generation High-Speed Communication and Energy-Circulating Computing Architecture Based on Photonic Transmission, Predictive Signal Recovery, Reversible Computation, and Thermodynamic Energy Recycling Driven by Quantum Thought Circuit OS ASI Part III: Integrated Study on Hardware-Rooted Trust, Deterministic Execution Control, Dynamic Regulatory Reconfiguration, and Digital Twin Verification Architecture Based on Quantum Thought Circuit OS ASI Part IV: Integrated Study on an Autonomous Adaptive Computing System・A Deterministic Control Architecture Based on Quantum Thought Circuit OS ASI for Dynamic Redefinition During Inference, Functional Conversion of Physical Properties, Regulatory Adaptation, Utilization of Degradation, Resource Circulation, and Deterministic Control Part V: Integrated Study on a Dynamically Adaptive Distributed Computing Infrastructure. A Deterministic Control Architecture Based on Quantum Thought Circuit OS ASI for Free Space Optical Communication, Metabolic Thermal Control, Supply-Driven Scheduling, Legal Slicing, and Autonomous Distributed Resource Allocation Part VI: Integrated Study on a Heterogeneous TEE Confidential Computing Platform Based on Distributed Trust Anchors. A Deterministic Control Architecture Based on Quantum Thought Circuit OS ASI for Dynamic Hardware RoT, Heterogeneous TEE Quorum, Reverse Containment Monitoring, Zero-Knowledge Proofs, and Distributed Ledger Auditing
Quantum sensing, with measurement performance exceeding classical limits, boasts breakthrough application potential across multiple fields but faces traditional bottlenecks like device design, state manipulation, and signal processing. This paper focuses on the interdisciplinary area of AI (Artificial Intelligence)-empowered quantum sensing, systematically reviewing AI’s application progress in three core aspects: quantum sensor structure design, quantum state manipulation, and signal processing. It analyzes typical cases to interpret AI’s solutions to high-dimensional optimization and complex noise adaptation, extracting a data-driven technical paradigm that improves quantum sensors’ sensitivity, stability, and integration. Additionally, it identifies common challenges such as model interpretability and data dependence, and envisions future directions like physics-informed models and quantum-classical fusion, providing references for quantum sensing’s practicalization and next-generation intelligent system development.
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
The fusion of quantum computing and robotics represents a frontier in computational intelligence, promising to overcome the limitations of classical sensor fusion methods in precision, scalability, and real-time adaptability. This study proposes a hybrid quantum–classical framework designed to enhance the integration and synchronization of LiDAR, visual, and inertial sensor data, ultimately improving robotic perception and spatial awareness in complex environments. The model employs quantum-assisted optimization techniques to handle high-dimensional uncertainty, noise propagation, and data redundancy challenges inherent in multi-sensor processing. By leveraging variational quantum circuits and classical machine learning optimizers, the hybrid model achieves efficient data correlation and error minimization during sensor alignment. Benchmark experiments were conducted to evaluate the efficiency and precision of the proposed quantum-assisted sensor fusion system relative to conventional data integration algorithms. The findings reveal that hybrid quantum–classical systems yield substantial improvements in localization accuracy, temporal synchronization, and resilience to sensor noise, while maintaining computational feasibility within near-term quantum devices. This work highlights the potential of quantum-enhanced perception frameworks to accelerate the next generation of autonomous robotics, providing a foundation for adaptive control, intelligent navigation, and mission-critical decision-making under uncertainty.
Objective: This research aims to evaluate and synthesise the development of artificial intelligence (AI) technologies integrated with quantum computing, especially regarding processing efficiency, hardware energy efficiency, and digital communication security. Research Design & Methods: This research utilizes the Systematic Literature Review (SLR) method of 13 scientific articles published in the ETRI Journal's 2024 special issue titled “Next-Gen AI and Quantum Technology.” The literature was selected based on inclusion criteria that included relevance to quantum AI, presence of experimental data, and contribution to computational efficiency. Findings: The study results show that approaches such as Quantum Reinforcement Learning and Quantum Kernel Classifiers can improve training efficiency and classification accuracy. Spiking Neural Networks technology reduced power consumption in AI-SoC and edge device designs. At the same time, the Quantum Key Distribution system demonstrated an error rate as low as 0.62% with WDM filter integration. The AONet video anomaly detection model achieves up to 97% AUC with the combination of a residual autoencoder and an attention module architecture. Implications & Recommendations: These findings indicate that quantum AI has great potential to overcome the limitations of classical computing in real-time applications and large-scale systems. However, challenges related to quantum noise, hardware stability, and integration with classical systems still need to be addressed. This research recommends strengthening hybrid infrastructure, developing interoperability standards, and utilizing multi-core architectures to support processing efficiency and data security. Contribution & Value Added: This study significantly contributes by systematically mapping existing methodologies and experiments in quantum AI, establishing a conceptual framework for future research avenues, and incorporating quantum AI technologies into industry, edge computing, and upcoming security systems
Quantum Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.