Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Journal of Academic and Industrial Research Innovations(IJAIRI)

Cloud-Native Topological Quantum Computing Using Distributed Photonic Anyon Networks

Murali Krishna Pasupuleti

Published Apr 25, 2026• 0 citations
Publisher

Abstract

Abstract This study investigates a cloud-native framework for topological quantum computing in which distributed photonic quantum networks support anyon-mediated logical operations, validated through formal reasoning agents, proof agents and benchmark-oriented simulation. The central research problem concerns the gap between theoretically robust anyonic quantum information processing and the operational realities of distributed quantum infrastructure, where photonic loss, repeater latency, cloud orchestration overhead, service-chain anomalies and proof-level uncertainty jointly shape the viability of large-scale computation. The proposed framework treats topological protection, photonic entanglement distribution and cloud-native service governance as a single cyber-physical architecture rather than as separate hardware, software and mathematical layers. The method integrates a literature-derived, realistic benchmark dataset with analytic modelling of fidelity decay, logical error proxies, latency decomposition and validation confidence. The results indicate that a cloud-native photonic anyon network can reduce the logical error proxy from 3.48e-2 in baseline photonic distribution to 4.91e-3 while maintaining a lower latency profile than monolithic quantum job execution. The paper contributes a five-subtheme literature synthesis, a layered architectural model, equations linking entanglement fidelity to logical failure probability, and a validation workflow that converts tentative mathematical reasoning into machine-verifiable proof artefacts. The study concludes that cloud-native topological quantum computing is most credible when photonic networking, anyon code design, observability and proof checking are co-designed under explicit service-level and error-budget constraints. Keywords: cloud-native quantum computing; topological quantum computing; photonic quantum networks; anyons; distributed quantum computing; quantum repeaters; Kubernetes; formal proof agents; logical error modelling; quantum service mesh. References de Andrade, MG 2021, 'A quantum walk control plane for distributed quantum computing in quantum networks', 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 313-323, doi: 10.1109/QCE52317.2021.00048. Pasupuleti, MK 2021a, 'A Comparative Computational Study of OR, Stochastic Programming, and Deep RL for Sustainable Cloud and Network Operations', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 149-173, doi: 10.62311/nesx/rp-m7-dec-2021. Pasupuleti, MK 2021b, 'Deep Reinforcement Learning for Online Cloud Resource Management: An Approximate Dynamic Programming Perspective', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 76-98, doi: 10.62311/nesx/rp-m4-dec-2021. Pasupuleti, MK 2021c, 'Global Convergence Guarantees of Iterative Algorithms for Unitary Quantum Channel Search', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 01, National Education Services, pp. 331-341, doi: 10.62311/nesx/rp-1-08-2021. Pasupuleti, MK 2021d, 'Hierarchical OR + RL Control for Cloud and Network Operations Under Policy-as-Code Constraints', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 99-123, doi: 10.62311/nesx/rp-m5-dec-2021. Pasupuleti, MK 2021e, 'Operations Research Challenges in Energy-Efficient Cloud and Network Infrastructure: An Integrated AI-Simulation Perspective', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 1-25, doi: 10.62311/nesx/rp-m1-dec-2021. Pasupuleti, MK 2021f, 'Policy-Aware AI for Cloud Operations: Integrating Deep RL with Cloud Custodian', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 124-148, doi: 10.62311/nesx/rp-m6-dec-2021. Pasupuleti, MK 2021g, 'Quantum-Safe Network Security: Advances in Quantum Cryptography and Systems Integration', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 01, National Education Services, pp. 232-245, doi: 10.62311/nesx/rp-1-01-2021. Pasupuleti, MK 2021h, 'Stochastic Modelling and Scenario Generation for Cloud Workload and Network Traffic in OR Models', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 26-50, doi: 10.62311/nesx/rp-m2-dec-2021. Pasupuleti, MK 2021i, 'Stochastic Optimization Models for Energy-Aware Cloud Resource Allocation Using Simulation-Derived Scenarios', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 51-75, doi: 10.62311/nesx/rp-m3-dec-2021. Yan, Z, Wang, Y-C, Ma, N, Qi, Y & Meng, ZY 2021, 'Topological phase transition and single/multi anyon dynamics of Z2 spin liquid', npj Quantum Materials, pp. 6, 39, doi: 10.1038/s41535-021-00338-1. Pasupuleti, MK 2022a, 'Building a Data-Driven Operations Backbone: Governance and Performance of Big-Data Pipelines with Hadoop and Apache Spark in Cloud Firms', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 53-72, doi: 10.62311/nesx/rp-m3-dec-2022. Pasupuleti, MK 2022b, 'Deep Learning Through a Statistical Lens: Approximation Guarantees, Training Dynamics, and Generative Models', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 125-143, doi: 10.62311/nesx/rp-1-01-2022. Pasupuleti, MK 2022c, 'Driven Resource Management in Cloud Services: Managerial Implications of Deep Reinforcement Learning Policies', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 1-28, doi: 10.62311/nesx/rp-m1-dec-2022. Pasupuleti, MK 2022d, 'Entropy-Driven Optimization of Multi-LLM Dialogues through Conditional Information Dynamics', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 157-167, doi: 10.62311/nesx/rp-2-01-2022. Pasupuleti, MK 2022e, 'Policy-as-Code in Cloud Governance: Impacts of Cloud Custodian on Cost, Compliance, and Managerial Control', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 29-52, doi: 10.62311/nesx/rp-m2-dec-2022. Pasupuleti, MK 2022f, 'Strategic Siting of Data Centres: A GIS-Based Multi-Criteria Decision Framework for Cost, Risk, and Sustainability', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 92-110, doi: 10.62311/nesx/rp-m5-dec-2022. Pasupuleti, MK 2022g, 'Unifying Topological Quantum Field Theories with Statistical Mechanics: A Framework for Quantum States and Dynamics', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 168-178, doi: 10.62311/nesx/rp-3-01-2022. Buckley, A, Chuprikov, P, Otoni, R, Rand, R, Soule, R & Eugster, P 2023, 'Towards an Algebraic Specification of Quantum Networks', Proceedings of the 1st Workshop on Quantum Networks and Distributed Quantum Computing, pp. 7-12, doi: 10.1145/3610251.3610557. Ellison, TD, Chen, Y-A, Dua, A, Shirley, W, Tantivasadakarn, N & Williamson, DJ 2023, 'Pauli topological subsystem codes from Abelian anyon theories', Quantum, pp. 7, 1137, doi: 10.22331/q-2023-10-12-1137. Pasupuleti, MK 2023a, 'AI Digital Twins in Simulink: Safe RL for Hardware-in-the-Loop Control', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 114-125, doi: 10.62311/nesx/rp-30623-114-125. Pasupuleti, MK 2023b, 'Edge AI with Mosquitto: Secure, QoS-Aware Federated Anomaly Detection', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 195-210, doi: 10.62311/nesx/rp-30623-195-210. Pasupuleti, MK 2023c, 'Foundation Models in PyTorch: Retrieval-Augmented Fine-Tuning with Safety Guardrail', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 03, National Education Services, pp. 1-16, doi: 10.62311/nesx/rp-30032023-1-16. Pasupuleti, MK 2023d, 'NS-3 for 6G: Multi-Agent RL Slicing with eBPF Telemetry', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 162-177, doi: 10.62311/nesx/rp-30623-162-177. Pasupuleti, MK 2023e, 'Policy-as-Code AI: Auto-Remediation and FinOps Risk Guardrails with Cloud Custodian', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 211-225, doi: 10.62311/nesx/rp-30623-211-225. Pasupuleti, MK 2023f, 'QML for Edge Anomaly Detection: Variational Circuits on MQTT/NS-3 Stream', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 12, National Education Services, pp. 174-188, doi: 10.62311/nesx/rp-301223-174-188. Pasupuleti, MK 2023g, 'Quantum-Inspired Optimizers for Cloud-Fog Scheduling: Near-Optimal SLAs under Energy Caps', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 03, National Education Services, pp. 17-28, doi: 10.62311/nesx/rp-30032023-17-28. Pasupuleti, MK 2023h, 'Quantum-Inspired Scheduling in CloudSim: Energy-Aware Cloud-Fog Orchestration', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 178-1

DOI
10.62311/nesx/rp8-30042026

Related papers

Fault-Tolerant Distributed Quantum Intelligence with Photonic Circuit Emulation and AI-Augmented Control for Autonomous Cyber-Physical Networks

Murali Krishna Pasupuleti

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

International Journal of Academic and Industrial Research Innovations(IJAIRI)•Sep 30, 2026•0 citations

An Optimized Quantum Computing Integration Approach for Multi-Cloud Environments: Architecture Design for Efficiency, Security, and Intelligent Workload Management

Dr. Natsuki Ono, Dr. Riku Ando

The integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.

European Journal of Emerging Cloud and Quantum Computing•Jul 1, 2026•0 citations

Topological Optical Networks for Stable Photonic Quantum Computation

Jincheng Zhang

This paper investigates the potential of topological optical networks for realizing stable photonic quantum computation. The core claim centers on leveraging the non-Abelian anyonic effects within these networks to mitigate noise, a significant challenge in traditional photonic quantum computing. The proposed mechanism involves constructing a quantum computing system utilizing topological optical networks, specifically targeting the reduction of decoherence and errors. This approach offers a novel pathway toward robust quantum computation by harnessing the unique topological properties of the network. The research explores key aspects of this system, including network design, control strategies, and error correction protocols, emphasizing the advantages of topological protection. The theoretical framework presented demonstrates the feasibility of this method and highlights its potential to advance the field of quantum information processing. Further research is needed to fully realize the capabilities of this system.

•Aug 29, 2026•0 citations

Quantum-AI Innovation and Intelligent Optimization for Global Economic, Industrial and Service-Sector Transformation

Murali Krishna Pasupuleti

Abstract: The accelerating convergence of artificial intelligence, quantum and quantum-inspired computation, digital twins, advanced sensing, secure connectivity and operations research creates a new design space for transforming economic systems, industrial production and service delivery. Yet the literature remains fragmented across manufacturing, infrastructure, networks, public administration and domain-specific optimization. This paper develops a unified Quantum-AI Innovation and Optimization for Transformation (QAIOT) framework that links data intelligence, quantum-enhanced search, multi-objective optimization, sectoral constraints, governance and measurable transformation outcomes. The study adopts a model-based analytical design and synthesizes only the references supplied in the source corpus. A mathematical formulation combines state-space dynamics, matrix coupling, constrained multi-objective optimization, Bayesian risk adjustment and a composite transformation index. The numerical demonstration uses normalized 0–10 values to show how productivity, resilience, sustainability, service quality, innovation capacity and institutional trust can be integrated without presenting the numerical outputs as empirical findings. The analysis indicates that the greatest conceptual value of Quantum-AI is not a universal computational speedup, but selective acceleration of difficult search, scheduling, allocation, network and simulation problems embedded within AI-driven decision pipelines. Sectoral benefits depend on data quality, interoperability, compute economics, cybersecurity, governance and human oversight. The paper contributes an extensible cross-sector architecture, a prior-art matrix, a transparent numerical scoring model and an implementation roadmap for testing Quantum-AI systems in manufacturing, energy, finance, logistics, healthcare, public services and digital service ecosystems. The framework is intended to support rigorous empirical validation and responsible deployment rather than unsupported claims of quantum advantage. Keywords: Quantum-AI; intelligent optimization; industrial transformation; service-sector transformation; digital servitization; quantum-inspired optimization; decision intelligence; multi-objective optimization; digital twins; autonomous systems; economic transformation; resilience; sustainability; AI-native infrastructure; governance

International Journal of Academic and Industrial Research Innovations(IJAIRI)•Sep 30, 2026•0 citations

Quantum-Accelerated Intelligence: Hybrid AI Systems for National-Scale Advantage

Murali Krishna Pasupuleti

Abstract: This study investigates quantum-accelerated intelligence as a national capability that integrates hybrid artificial intelligence with quantum and quantum-inspired optimisation. In many public infrastructures, predictive models deliver situational awareness, but operational value is constrained by hard, time-critical optimisation tasks such as scheduling, routing, and resource allocation under safety and equity constraints. Quantum optimisation is treated as a modular accelerator for selected subproblems rather than as a universal replacement for classical planning. A cross-layer reference architecture is specified to connect interoperable data products, trustworthy model operations, and optimisation services across cloud-to-edge deployments. To support transparent benchmarking, a Quantum-Accelerated Intelligence Readiness Index (QAIRI) is introduced using real, open indicators from the World Bank World Development Indicators (WDI). The index is designed to diagnose ecosystem bottlenecks across innovation capacity, secure digital infrastructure, sustainability constraints, and absorption potential. Results show substantial dispersion in readiness across major economies, with stronger readiness associated with balanced progress across research capacity, digital trust infrastructure, and renewable integration. The findings motivate a cautious-acceleration roadmap: mission pilots with measurable baselines, procurement rules that require evidence and auditability, and policy-as-code guardrails that reduce operational risk while enabling public value at scale. Keywords: quantum optimisation; hybrid AI; national infrastructure; trustworthy AI; digital twins; TwinOps; green compute; governance; cloud-to-edge; resilience. ______________________________________________________________________________

International Journal of Academic and Industrial Research Innovations(IJAIRI)•Dec 24, 2025•1 citation

Topological Quantum Computing: A Novel Approach to Heterogeneous Qubit Connectivity

Jincheng Zhang

This paper explores a novel approach to achieving high-fidelity and scalable quantum computation through heterogeneous qubit connectivity within a topological quantum computing framework. The core challenge in topological quantum computing lies in effectively connecting qubits of diverse types – differing in their physical realization and control mechanisms – to enable complex quantum algorithms. This work proposes a system leveraging topological networks to establish these connections, capitalizing on quantum entanglement to facilitate information transfer and control across disparate qubit architectures. The proposed method addresses a critical bottleneck in the development of topological quantum computers by offering a fundamentally new strategy for heterogeneous qubit integration. The theoretical framework presented outlines the key principles and potential advantages of this approach, emphasizing the realization of robust, scalable, and versatile quantum systems. We detail the conceptual design, focusing on entanglement distribution and error mitigation strategies specific to this architecture. The ultimate goal is to demonstrate a pathway towards building quantum computers capable of tackling complex problems requiring diverse qubit capabilities.

•Sep 3, 2026•0 citations
Abstract This study investigates a cloud-native framework for topological quantum computing in which distributed photonic quantum networks support anyon-mediated logical operations, validated through formal reasoning agents, proof agents and benchmark-oriented simulation. The central research problem concerns the gap between theoretically robust anyonic quantum information processing and the operational realities of distributed quantum infrastructure, where photonic loss, repeater latency, cloud orchestration overhead, service-chain anomalies and proof-level uncertainty jointly shape the viability of large-scale computation. The proposed framework treats topological protection, photonic entanglement distribution and cloud-native service governance as a single cyber-physical architecture rather than as separate hardware, software and mathematical layers. The method integrates a literature-derived, realistic benchmark dataset with analytic modelling of fidelity decay, logical error proxies, latency decomposition and validation confidence. The results indicate that a cloud-native photonic anyon network can reduce the logical error proxy from 3.48e-2 in baseline photonic distribution to 4.91e-3 while maintaining a lower latency profile than monolithic quantum job execution. The paper contributes a five-subtheme literature synthesis, a layered architectural model, equations linking entanglement fidelity to logical failure probability, and a validation workflow that converts tentative mathematical reasoning into machine-verifiable proof artefacts. The study concludes that cloud-native topological quantum computing is most credible when photonic networking, anyon code design, observability and proof checking are co-designed under explicit service-level and error-budget constraints. Keywords: cloud-native quantum computing; topological quantum computing; photonic quantum networks; anyons; distributed quantum computing; quantum repeaters; Kubernetes; formal proof agents; logical error modelling; quantum service mesh. References de Andrade, MG 2021, 'A quantum walk control plane for distributed quantum computing in quantum networks', 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 313-323, doi: 10.1109/QCE52317.2021.00048. Pasupuleti, MK 2021a, 'A Comparative Computational Study of OR, Stochastic Programming, and Deep RL for Sustainable Cloud and Network Operations', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 149-173, doi: 10.62311/nesx/rp-m7-dec-2021. Pasupuleti, MK 2021b, 'Deep Reinforcement Learning for Online Cloud Resource Management: An Approximate Dynamic Programming Perspective', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 76-98, doi: 10.62311/nesx/rp-m4-dec-2021. Pasupuleti, MK 2021c, 'Global Convergence Guarantees of Iterative Algorithms for Unitary Quantum Channel Search', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 01, National Education Services, pp. 331-341, doi: 10.62311/nesx/rp-1-08-2021. Pasupuleti, MK 2021d, 'Hierarchical OR + RL Control for Cloud and Network Operations Under Policy-as-Code Constraints', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 99-123, doi: 10.62311/nesx/rp-m5-dec-2021. Pasupuleti, MK 2021e, 'Operations Research Challenges in Energy-Efficient Cloud and Network Infrastructure: An Integrated AI-Simulation Perspective', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 1-25, doi: 10.62311/nesx/rp-m1-dec-2021. Pasupuleti, MK 2021f, 'Policy-Aware AI for Cloud Operations: Integrating Deep RL with Cloud Custodian', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 124-148, doi: 10.62311/nesx/rp-m6-dec-2021. Pasupuleti, MK 2021g, 'Quantum-Safe Network Security: Advances in Quantum Cryptography and Systems Integration', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 01, National Education Services, pp. 232-245, doi: 10.62311/nesx/rp-1-01-2021. Pasupuleti, MK 2021h, 'Stochastic Modelling and Scenario Generation for Cloud Workload and Network Traffic in OR Models', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 26-50, doi: 10.62311/nesx/rp-m2-dec-2021. Pasupuleti, MK 2021i, 'Stochastic Optimization Models for Energy-Aware Cloud Resource Allocation Using Simulation-Derived Scenarios', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 01, no. 12, National Education Services, pp. 51-75, doi: 10.62311/nesx/rp-m3-dec-2021. Yan, Z, Wang, Y-C, Ma, N, Qi, Y & Meng, ZY 2021, 'Topological phase transition and single/multi anyon dynamics of Z2 spin liquid', npj Quantum Materials, pp. 6, 39, doi: 10.1038/s41535-021-00338-1. Pasupuleti, MK 2022a, 'Building a Data-Driven Operations Backbone: Governance and Performance of Big-Data Pipelines with Hadoop and Apache Spark in Cloud Firms', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 53-72, doi: 10.62311/nesx/rp-m3-dec-2022. Pasupuleti, MK 2022b, 'Deep Learning Through a Statistical Lens: Approximation Guarantees, Training Dynamics, and Generative Models', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 125-143, doi: 10.62311/nesx/rp-1-01-2022. Pasupuleti, MK 2022c, 'Driven Resource Management in Cloud Services: Managerial Implications of Deep Reinforcement Learning Policies', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 1-28, doi: 10.62311/nesx/rp-m1-dec-2022. Pasupuleti, MK 2022d, 'Entropy-Driven Optimization of Multi-LLM Dialogues through Conditional Information Dynamics', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 157-167, doi: 10.62311/nesx/rp-2-01-2022. Pasupuleti, MK 2022e, 'Policy-as-Code in Cloud Governance: Impacts of Cloud Custodian on Cost, Compliance, and Managerial Control', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 29-52, doi: 10.62311/nesx/rp-m2-dec-2022. Pasupuleti, MK 2022f, 'Strategic Siting of Data Centres: A GIS-Based Multi-Criteria Decision Framework for Cost, Risk, and Sustainability', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 12, National Education Services, pp. 92-110, doi: 10.62311/nesx/rp-m5-dec-2022. Pasupuleti, MK 2022g, 'Unifying Topological Quantum Field Theories with Statistical Mechanics: A Framework for Quantum States and Dynamics', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 02, no. 01, National Education Services, pp. 168-178, doi: 10.62311/nesx/rp-3-01-2022. Buckley, A, Chuprikov, P, Otoni, R, Rand, R, Soule, R & Eugster, P 2023, 'Towards an Algebraic Specification of Quantum Networks', Proceedings of the 1st Workshop on Quantum Networks and Distributed Quantum Computing, pp. 7-12, doi: 10.1145/3610251.3610557. Ellison, TD, Chen, Y-A, Dua, A, Shirley, W, Tantivasadakarn, N & Williamson, DJ 2023, 'Pauli topological subsystem codes from Abelian anyon theories', Quantum, pp. 7, 1137, doi: 10.22331/q-2023-10-12-1137. Pasupuleti, MK 2023a, 'AI Digital Twins in Simulink: Safe RL for Hardware-in-the-Loop Control', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 114-125, doi: 10.62311/nesx/rp-30623-114-125. Pasupuleti, MK 2023b, 'Edge AI with Mosquitto: Secure, QoS-Aware Federated Anomaly Detection', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 195-210, doi: 10.62311/nesx/rp-30623-195-210. Pasupuleti, MK 2023c, 'Foundation Models in PyTorch: Retrieval-Augmented Fine-Tuning with Safety Guardrail', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 03, National Education Services, pp. 1-16, doi: 10.62311/nesx/rp-30032023-1-16. Pasupuleti, MK 2023d, 'NS-3 for 6G: Multi-Agent RL Slicing with eBPF Telemetry', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 162-177, doi: 10.62311/nesx/rp-30623-162-177. Pasupuleti, MK 2023e, 'Policy-as-Code AI: Auto-Remediation and FinOps Risk Guardrails with Cloud Custodian', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 211-225, doi: 10.62311/nesx/rp-30623-211-225. Pasupuleti, MK 2023f, 'QML for Edge Anomaly Detection: Variational Circuits on MQTT/NS-3 Stream', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 12, National Education Services, pp. 174-188, doi: 10.62311/nesx/rp-301223-174-188. Pasupuleti, MK 2023g, 'Quantum-Inspired Optimizers for Cloud-Fog Scheduling: Near-Optimal SLAs under Energy Caps', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 03, National Education Services, pp. 17-28, doi: 10.62311/nesx/rp-30032023-17-28. Pasupuleti, MK 2023h, 'Quantum-Inspired Scheduling in CloudSim: Energy-Aware Cloud-Fog Orchestration', International Journal of Academic and Industrial Research Innovations (IJAIRI), vol. 03, no. 06, National Education Services, pp. 178-1
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 integration of quantum computing into multi-cloud environments represents a transformative paradigm in distributed computing, offering substantial improvements in computational efficiency, security resilience, and adaptive workload orchestration. However, the heterogeneous nature of cloud infrastructures, combined with the nascent and resource-sensitive characteristics of quantum systems, introduces significant architectural, operational, and security challenges. This paper proposes an optimized quantum computing integration approach designed specifically for multi-cloud ecosystems, emphasizing scalable architecture design, intelligent workload distribution, and enhanced cryptographic security mechanisms.The study synthesizes foundational quantum computing principles with contemporary multi-cloud orchestration strategies to develop a hybrid architectural framework. The proposed model leverages quantum-enhanced optimization techniques, including quantum approximate optimization principles, to dynamically allocate computational tasks across distributed cloud nodes. Additionally, it integrates quantum-safe security protocols to mitigate emerging cyber threats in cloud environments. Prior research highlights the relevance of such integration, particularly in solving complex optimization problems and enhancing system-level security (Brown & White, 2022; Cao et al., 2020).A structured literature synthesis reveals that while cloud computing and quantum computing have individually matured, their convergence remains underexplored in practical deployment contexts. This paper addresses this gap by introducing an architecture that supports adaptive workload balancing, quantum-classical hybrid execution layers, and secure inter-cloud communication channels. The findings indicate that quantum-enhanced orchestration can significantly reduce computational latency and improve resource utilization efficiency in multi-cloud systems.
This paper investigates the potential of topological optical networks for realizing stable photonic quantum computation. The core claim centers on leveraging the non-Abelian anyonic effects within these networks to mitigate noise, a significant challenge in traditional photonic quantum computing. The proposed mechanism involves constructing a quantum computing system utilizing topological optical networks, specifically targeting the reduction of decoherence and errors. This approach offers a novel pathway toward robust quantum computation by harnessing the unique topological properties of the network. The research explores key aspects of this system, including network design, control strategies, and error correction protocols, emphasizing the advantages of topological protection. The theoretical framework presented demonstrates the feasibility of this method and highlights its potential to advance the field of quantum information processing. Further research is needed to fully realize the capabilities of this system.
Abstract: The accelerating convergence of artificial intelligence, quantum and quantum-inspired computation, digital twins, advanced sensing, secure connectivity and operations research creates a new design space for transforming economic systems, industrial production and service delivery. Yet the literature remains fragmented across manufacturing, infrastructure, networks, public administration and domain-specific optimization. This paper develops a unified Quantum-AI Innovation and Optimization for Transformation (QAIOT) framework that links data intelligence, quantum-enhanced search, multi-objective optimization, sectoral constraints, governance and measurable transformation outcomes. The study adopts a model-based analytical design and synthesizes only the references supplied in the source corpus. A mathematical formulation combines state-space dynamics, matrix coupling, constrained multi-objective optimization, Bayesian risk adjustment and a composite transformation index. The numerical demonstration uses normalized 0–10 values to show how productivity, resilience, sustainability, service quality, innovation capacity and institutional trust can be integrated without presenting the numerical outputs as empirical findings. The analysis indicates that the greatest conceptual value of Quantum-AI is not a universal computational speedup, but selective acceleration of difficult search, scheduling, allocation, network and simulation problems embedded within AI-driven decision pipelines. Sectoral benefits depend on data quality, interoperability, compute economics, cybersecurity, governance and human oversight. The paper contributes an extensible cross-sector architecture, a prior-art matrix, a transparent numerical scoring model and an implementation roadmap for testing Quantum-AI systems in manufacturing, energy, finance, logistics, healthcare, public services and digital service ecosystems. The framework is intended to support rigorous empirical validation and responsible deployment rather than unsupported claims of quantum advantage. Keywords: Quantum-AI; intelligent optimization; industrial transformation; service-sector transformation; digital servitization; quantum-inspired optimization; decision intelligence; multi-objective optimization; digital twins; autonomous systems; economic transformation; resilience; sustainability; AI-native infrastructure; governance
Abstract: This study investigates quantum-accelerated intelligence as a national capability that integrates hybrid artificial intelligence with quantum and quantum-inspired optimisation. In many public infrastructures, predictive models deliver situational awareness, but operational value is constrained by hard, time-critical optimisation tasks such as scheduling, routing, and resource allocation under safety and equity constraints. Quantum optimisation is treated as a modular accelerator for selected subproblems rather than as a universal replacement for classical planning. A cross-layer reference architecture is specified to connect interoperable data products, trustworthy model operations, and optimisation services across cloud-to-edge deployments. To support transparent benchmarking, a Quantum-Accelerated Intelligence Readiness Index (QAIRI) is introduced using real, open indicators from the World Bank World Development Indicators (WDI). The index is designed to diagnose ecosystem bottlenecks across innovation capacity, secure digital infrastructure, sustainability constraints, and absorption potential. Results show substantial dispersion in readiness across major economies, with stronger readiness associated with balanced progress across research capacity, digital trust infrastructure, and renewable integration. The findings motivate a cautious-acceleration roadmap: mission pilots with measurable baselines, procurement rules that require evidence and auditability, and policy-as-code guardrails that reduce operational risk while enabling public value at scale. Keywords: quantum optimisation; hybrid AI; national infrastructure; trustworthy AI; digital twins; TwinOps; green compute; governance; cloud-to-edge; resilience. ______________________________________________________________________________
This paper explores a novel approach to achieving high-fidelity and scalable quantum computation through heterogeneous qubit connectivity within a topological quantum computing framework. The core challenge in topological quantum computing lies in effectively connecting qubits of diverse types – differing in their physical realization and control mechanisms – to enable complex quantum algorithms. This work proposes a system leveraging topological networks to establish these connections, capitalizing on quantum entanglement to facilitate information transfer and control across disparate qubit architectures. The proposed method addresses a critical bottleneck in the development of topological quantum computers by offering a fundamentally new strategy for heterogeneous qubit integration. The theoretical framework presented outlines the key principles and potential advantages of this approach, emphasizing the realization of robust, scalable, and versatile quantum systems. We detail the conceptual design, focusing on entanglement distribution and error mitigation strategies specific to this architecture. The ultimate goal is to demonstrate a pathway towards building quantum computers capable of tackling complex problems requiring diverse qubit capabilities.