This paper proposes a novel distributed consensus algorithm inspired by quantum mechanics, termed the Quantum-Inspired Distributed Consensus Algorithm with Measurement-Based Feedback (QIDCA-MBF). The core idea is to utilize the principles of quantum superposition to accelerate the convergence of distributed consensus in challenging network environments, particularly those prone to node failures. Unlike traditional consensus algorithms, QIDCA-MBF employs probabilistic representations of proposed values within each node, mimicking the concept of quantum superposition. A key innovation is the incorporation of measurement-based feedback, modeled after quantum measurement, to collapse these superpositions and guide the nodes towards a shared consensus value. This feedback mechanism dynamically adapts to the network topology and detects node failures, significantly enhancing the algorithm's robustness and convergence speed. The algorithm is formulated based on a modified averaging process, incorporating probabilistic weights derived from the superposition states. Simulation results demonstrate the effectiveness of QIDCA-MBF in achieving consensus rapidly and reliably, outperforming conventional distributed consensus protocols under various failure scenarios. The algorithm's adaptability and resilience make it a promising candidate for applications in decentralized systems, sensor networks, and blockchain technologies.
This paper proposes a universal post quantum privacy protection edge identity authentication framework to address the challenges faced by edge identity authentication in distributed cross domain networks, such as quantum attack threats, cross domain data privacy breaches, and difficulties in coordinating anonymity protection and compliance supervision. The framework adopts an optimized lattice based linkable ring signature protocol to meet the lightweight operation requirements of edge nodes and prevent the risk of leakage in identity data interaction; Design traceability constraints and controllable cross domain traceability mechanisms based on the linkability feature of signatures, balancing user privacy and regulatory requirements. Prove that the scheme possesses unforgeability, strong anonymity, and quantum resistance under the random oracle model. After optimizing the algorithm and interaction logic, the authentication efficiency is improved by 8% to 15% compared to similar solutions, and it is adapted to the low computing power and low latency characteristics of edge nodes. Combining zero knowledge proof to build a lightweight data collection mechanism and achieve privacy protection throughout the entire data process. This article uses the integrated aviation tourism system as a typical application case to verify that the proposed framework can be widely applied to various distributed cross domain networks and identity authentication systems.
In the era of quantum computing, data sharing in the Internet of Vehicles (IoV) confronts the challenges of auditability, efficiency, and quantum security. However, existing research remains insufficient to meet the requirements of high mobility, resource constraints, and resilience against quantum attacks. In this paper, we propose a new quantum-secure auditable data sharing framework, in which we first present a quantum-resistant puncturable signature algorithm (QRPPRFS). Combining the low-noise LPN-based pseudorandom function with an optimized trapdoor generation mechanism, it achieves compact key sizes and millisecond-level signing; second, the blockchain and dual-commitment proof mechanism are integrated to ensure anonymity, transparent auditability and robustness. Finally, we rigorously demonstrate the correctness of our scheme, the EUF-CMA with puncturing of QRPPRFS, and the knowledge soundness and witness zero-knowledge of the dual-commitment proof system. Experimental evaluations show that, under the practical setting$n=256$and$q \approx 2^{23}$, the proposed scheme keeps both signing and verification latencies below 10 ms, and reduces the initial secret-key storage to only 0.22 MB. These results demonstrate that the proposed scheme achieves both enhanced security and high efficiency, outperforming existing schemes.
KRILL β Bio-Inspired Architecture for IoT Consensus Decentralized IoT consensus without blockchain β inspired by ant colonies, immune systems & chemical diffusion. What is KRILL? The problem: Blockchain doesn't work for IoT. It's too heavy, too slow, and too expensive for devices running on batteries with 32KB of RAM. IoT needs to answer "What is the physical state of the world?" β not "Who has how much money?" The solution: KRILL replaces blockchain with 9 mechanisms borrowed from biology: Mechanism Biological inspiration What it does Stigmergic Consensus Ant pheromone trails Nodes "deposit" readings like ants deposit pheromones. Truth emerges from convergence, not voting. Pentastratic Immune System Human immune layers 5-layer anomaly detection: skin (format check) β innate (statistical) β adaptive (learned) β NK audit β autoimmune suppression. Metabolic State Cell metabolism Data has a "half-life" β old readings decay and die automatically. No infinite ledger. Entropic Data Valuation Thermodynamic entropy Network autonomously decides which data is worth storing based on information theory. Quorum Sensing Bacterial quorum sensing Nodes detect local density and switch modes (solo β quorum β swarm) without any coordinator. Horizontal Gene Transfer Bacterial gene sharing Firmware updates spread node-to-node like genes between bacteria. No update server needed. Morphogenetic Topology Embryonic development Network self-organizes its topology using reaction-diffusion (Turing patterns). Thymic Tolerance T-cell training in thymus System learns what "normal" looks like to avoid false alarms. Immunological Memory Vaccine/antibody memory Once the network detects an attack pattern, it "vaccinates" all nodes. The result: 1000x less energy than blockchain consensus Runs on a $2 ESP32 microcontroller (240KB RAM) Works with intermittent connectivity (mesh, BLE, LoRa, WiFi) No miners, no staking, no tokens β consensus is grounded in physical reality Scales to millions of nodes without coordinator Status: Research paper + engineering specification. No working implementation yet. Documents Document Description Research Paper (HTML) Full academic paper β mathematical formalizations, energy analysis, novelty assessment, risk analysis. 20 sections. Open in browser β Print β Save as PDF. Engineering Specification (HTML) Implementation reference β byte-level wire formats, state machines, pseudocode, test vectors, transport layers. Ready to code from. Source files (Markdown): krill-bioinspired-architecture.md β Research paper krill-bia-engineering-spec.md β Engineering spec Architecture at a Glance βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ β KRILL Node (ESP32) β ββββββββββββ¬βββββββββββ¬βββββββββββ¬βββββββββββ¬ββββββββββββββ€ β Stigmer- β Immune β Metabolicβ Quorum β Morpho- β β gic β System β State β Sensing β genetic β β Consensusβ (5-layer)β (decay) β (modes) β Topology β ββββββββββββ΄βββββββββββ΄βββββββββββ΄βββββββββββ΄ββββββββββββββ€ β Transport: BLE mesh / WiFi / LoRa β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ€ β PUF Identity + Ed25519 Enrollment β βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ MVP β Where to Start If you want to implement KRILL, start with these 4 subsystems (the rest can be added later): ES-13 β Cryptographic enrollment (PUF + Ed25519 identity) ES-12 β Transport layer (BLE mesh for local, WiFi for bridging) ES-1 β Core data types and wire formats ES-3 β Stigmergic Consensus (the core algorithm) ES-10 β Main event loop and message dispatch Target hardware: ESP32 (Nano node) + nRF52840 (Dust node, optional) Why Not Blockchain? Blockchain (e.g. Ethereum) KRILL-BIA Consensus energy ~50 Wh/tx (PoW) or ~0.01 Wh/tx (PoS) ~0.00001 Wh/tx Minimum RAM 512MB+ 32KB (Dust), 240KB (Nano) State growth Infinite (append-only) Bounded (data decays) Offline tolerance Minutes before fork Days (pheromone half-life) Hardware cost $50+ SBC $2 ESP32 Finality Probabilistic (blocks) Convergent (pheromone field) Key Innovation: Physical-World Consensus Grounding Unlike blockchain where consensus is purely computational, KRILL grounds consensus in physical reality: Sensor readings must be physically plausible (a thermometer can't jump 50C in 1 second) Nodes that are physically closer have more weight (radio signal strength = distance proxy) The laws of physics constrain what values are possible β this is a defense layer that doesn't exist in financial systems This means an attacker must not only compromise the software but also defeat physics β a fundamentally harder problem. Contributing See CONTRIBUTING.md for how to get involved. Areas where help is most needed: Rust/C firmware for ESP32 (core protocol implementation) Simulation β model pheromone convergence with 100-10,000 virtual nodes Hardware testing β BLE mesh range, LoRa timing, PUF enrollment on real chips Security review β formal verification of immune system thresholds Documentation β diagrams, tutorials, translations License This project is licensed under the MIT License. Supporting This Work If KRILL is useful to your research or organization, consider supporting further development: ETH / ERC-20 / Base / Arbitrum / Polygon: 0x0BC290355c0B16B5B247701B7BC9AB2E1e61ffa7 Funds go toward: Reference firmware for ESP32 + nRF52840 Hardware test beds (100-node BLE mesh) Independent security audits Bug bounty program for protocol vulnerabilities Code contributions are equally welcome β see CONTRIBUTING.md.
This paper proposes a unified Layer-0 infrastructure protocol for post-quantum distributed computing, based on high-dimensional coordinate representations derived from non-commensurate Mersenne primes. Unlike traditional approaches reliant on block-based ledgers or persistent state replication, the proposed Mersenne Lattice Protocol (MLP) represents data, transactions, and authority states as coordinates within a high-dimensional lattice space. By projecting computational events into a 4096-dimensional vector space, MLP enables theoretically unbounded parallel transaction processing under resonance-based validation, while simultaneously eliminating permanent state storage at the protocol level. Furthermore, the protocol integrates Heart Rate Variability (HRV) as a dynamic physiological entropy source for stateless bio-key regeneration, thereby binding cryptographic authority to real-time biological liveness and spatiotemporal context. Functional prototypes of the core MLP architecture have been implemented and verified through a live demo environment (https://www.icekey.cloud/teleport_v), demonstrating peak throughput exceeding 45,000,000 TPS in a parallel resonance cluster. This framework provides the foundation for post-quantum secure financial systems, stateless media reconstruction, critical infrastructure protection, and delay-tolerant interplanetary communication.
Chandramohan Dhasarathan, Puviyarasi Thirugnanasammandamoorthi, B. Ramachandra Reddy, Diwakar Tripathi
The integration of Blockchain technology with the Internet of Medical Things (IoMT) presents transformative potential for healthcare, enhancing data security, privacy, and transparency. As IoMT devices collect and transmit sensitive health data, ensuring privacy and preventing unauthorized access become critical concerns. Blockchain offers a decentralized, immutable ledger that can address these challenges by providing secure transaction recording and audit trails. However, limitations related to scalability and efficiency remain obstacles to broad adoption. This research explores various optimization strategies such as consensus algorithm improvements (e.g., Proof of Stake over Proof of Work), hybrid Blockchain models, and off-chain storage to enhance performance in IoMT environments. Lightweight cryptographic protocols are also proposed to reduce device overhead. Through simulations and real-world case studies, we evaluate these strategies in terms of latency, energy efficiency, security, and compliance. Results indicate that Blockchain, when optimized, significantly enhances trust, interoperability, and usability in healthcare IoMT applications.
Digital Twin (DT) technology is elevating the next-generation intelligent transportation systems industry to new heights, as it enables real-time monitoring, predictive maintenance, and adaptive control of connected and autonomous vehicles. However, the use of GenAI and DTs in interconnected vehicular technology ecosystems introduces new attack vectors, particularly from quantum computing, which can easily break classical encryption systems. This paper introduces Reputation-based Proof-of-Stake (R-PoS), a hybrid consensus mechanism tailored for lattice-based PQC operations on vehicular edge devices. The core contribution is a lightweight hybrid consensus mechanism optimized for lattice-based PQC on edge devices, enabling secure and scalable synchronization between physical assets and their digital twins. Experimental results from a containerized IoT testbed using the Open Quantum Safe (OQS) library show that the proposed PQC-BC framework achieves an average throughput of 1178 transactions per second with latency of 0.78 second. These results affirm the framework's efficacy in securing future interconnected vehicular environments and establishing a trust foundation for sustainable quantum-resistant digital twin applications.
In this paper, we explored the Transaction Network on Ethereum from August 7, 2015 to November 22, 2024 with the perspective of complex network science. We aim to identify key network characteristics, such as disassortativity and core nodes stability. Our analysis reveals that high-degree nodes, typically mining pools and exchanges, preferentially connect with lowdegree nodes and rarely connected with each other. Additionally, we find that the ETN core structure remains robust over time. These findings contribute to blockchain network regulation and offer insights for future research into cryptocurrency transaction dynamics.
The rise of distributed ledger technologies (DLTs) has brought significant advancements in data security and decentralized transaction processing. However, adapting these technologies to mobile and resource-constrained environments remains a challenge. This article explores HC-LDL, a novel mobile distributed ledger designed to operate efficiently on mobile devices and facilitate Device-to-Device (D2D) communication. HC-LDL leverages two unique consensus protocols Proof-of Context (PoC) and Proof-of-Interchangeability (PoI) to address the limitations faced by traditional DLTs in mobile settings. PoC utilizes contextual data and user reputation to validate new blocks, ensuring secure and contextually relevant data addition. PoE periodically generates regenesis blocks that summarize and reorganize the ledger, optimizing storage and enhancing scalability. Through these mechanisms, HC-LDL provides a robust solution for mobile applications, offering secure, efficient, and decentralized data management. This article delves into the architecture, consensus protocols, and potential applications of HC-LDL, highlighting its innovative approach to mobile distributed ledger technology and D2D communication.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.
Imran Hussain, Hafiz Ashiq Hussain, Nasim Ullah, Stanislav MiΕ‘Γ‘k
An evolving energy system with a dispersed infrastructure may not be compatible with traditional centralized optimization and management techniques. Blockchain, a peer-to-peer immutable distributed ledger technology, has the potential to significantly contribute to the management of emerging trends of decentralized power networks. However, complex optimization problems associated with the decentralized power grid are poorly integrated into the existing blockchain applications. Here, we suggest Proof of Inherent Intelligence (PoII), a novel prosumer-centric consensus mechanism designed to assist multi-interest party optimization challenges of the distributed power grid. We demonstrate PoIIβs operation and performance with comprehensive mathematical modeling of energy pool-market trading and scheduling optimization problems. The efficiency of the proposed framework is evaluated against the existing blockchain applications for peer-to-peer energy transactions in terms of latency, throughput, tolerance against adversaries, vulnerability, and optimization capabilities. A thorough case study of the power grid that includes thermal, wind, and intermittent generation sources is presented to assess the effectiveness of the proposed consensus mechanism. Power demand, reserves, trading, and scheduling scenarios in both the day-ahead and balancing markets are among the peer-to-peer energy transactional elements that are assessed to support the efficacy of the suggested consensus approach.
Harun Jamil, Yang Jian, Faisal Jamil, Mohammad Hijjawi Β· 5 authors
This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy , including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization , task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization , enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers , and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.
Delphi Hanggoro, Jauzak Hussaini Windiatmaja, Abdul Muis, Riri Fitri Sari Β· 5 authors
This study addresses integrating blockchain technology into lightweight devices, specifically on clustered Wireless Sensor Networks (WSNs). Integrating blockchain in the WSNs solved the problem of heterogeneity, data integrity, and data confidentiality. However, no blockchain integration considers network lifetime in WSNs. This research focuses on developing a permissioned blockchain system that incorporates a consensus mechanism known as Proof-of-Authority (PoA) within clustered WSNs with two main features. The first is to enhance the network lifetime by introducing a rotational selection of block proposers using an Energy-Aware PoA (EA-PoA) weighting mechanism. Known as the Multi-Level Blockchain Model (MLBM), the subsequent feature is the creation of a hierarchical network model within a blockchain network. The MLBM network comprises both local and master blockchains. Each cluster inside a WSN possesses its local blockchain network. In MLBM, the local blockchain creates a block on the main blockchain by proposing the headers of every ten blocks to improve data integrity. Each local blockchain has its leader, which can increase block production. The results show that the proposed solution can overcome traditional PoA performance and is suitable for clustered WSNs. In terms of lifetime, the EA-PoA selection method can extend network lifetime by up to 10%. In addition, MLBM can increase block production by up to twice each additional cluster compared to a single blockchain network used in traditional PoA.
The cryptocurrency market has experienced remarkable growth and garnered widespread attention in recent years. Ethereum, as one of the most well-known cryptocurrencies, has faced criticism for its network congestion and high transaction fees. To address this issue, developers have proposed various scalability solutions. In this paper, we compare two different network solutions, Polygon and Arbitrum, to the Ethereum. Based on the research methodology of complex networks, this study encompasses various stages ranging from data collection and processing to the modeling and analysis of transaction networks. Our findings reveal that Arbitrum shows an advantage over Polygon when comparing the networks at the same transaction volume. With sufficient development, Rollups has the potential to become the preferred scaling solution for Ethereum. Additionally, our analysis illustrates that all transaction networks exhibit a small-world phenomenon when they reach a significant size.
A. Manju Priya, T. Biju Daniel, V. Padmapriya, S. Esther Praveena
Swarm intelligence, inspired by the collective behavior observed in social organisms, has emerged as a powerful paradigm in both natural and artificial systems. The concept of a swarm refers to a large group of simple agents that interact locally with one another and their environment, giving rise to complex and intelligent behavior at the group level. Swarm intelligence, on the other hand, represents the ability of a swarm to self-organize, adapt, and solve complex problems without central control. In nature, swarms of social insects such as bees, ants, termites, and birds exhibit remarkable abilities in foraging, navigation, resource allocation, and defense. These organisms demonstrate how the interactions of simple individuals can lead to efficient and robust solutions to various challenges faced in their environments. In artificial systems, researchers have successfully translated the principles of swarm intelligence into algorithms and techniques for optimization, decision-making, and problem-solving. Popular swarm intelligence algorithms, such as Ant Colony Optimization, Particle Swarm Optimization, and Artificial Bee Colony, have shown great promise in tackling complex optimization and search tasks. This paper provides an overview of the fundamental concepts of swarm intelligence and explores the similarities and differences between natural and artificial swarms. It delves into the principles of self-organization, decentralized decision-making, and adaptation that underpin swarm intelligence, allowing these systems to cope with dynamic and uncertain environments. Furthermore, the paper examines the application domains of swarm intelligence, ranging from robotics and autonomous systems to data clustering, image processing, and network routing. The potential of swarm robotics in solving real-world challenges, such as environmental monitoring, disaster response, and precision agriculture, is also explored. Swarm intelligence presents a compelling avenue for understanding and harnessing emergent collective behavior in both biological and computational contexts. The interplay of simplicity, local interactions, and adaptation enables swarms to tackle complex problems efficiently, making them a valuable source of inspiration for the design of intelligent systems in various fields. The study of swarm intelligence continues to advance, offering exciting possibilities for creating adaptive, robust, and scalable solutions in the ever-evolving landscape of artificial intelligence and beyond.
This paper proposes a blockchain-secured deep reinforcement learning (BC-DRL) optimization framework for {data management and} resource allocation in decentralized {wireless mobile edge computing (MEC)} networks. In our framework, {we design a low-latency reputation-based proof-of-stake (RPoS) consensus protocol to select highly reliable blockchain-enabled BSs to securely store MEC user requests and prevent data tampering attacks.} {We formulate the MEC resource allocation optimization as a constrained Markov decision process that balances minimum processing latency and denial-of-service (DoS) probability}. {We use the MEC aggregated features as the DRL input to significantly reduce the high-dimensionality input of the remaining service processing time for individual MEC requests. Our designed constrained DRL effectively attains the optimal resource allocations that are adapted to the dynamic DoS requirements. We provide extensive simulation results and analysis to} validate that our BC-DRL framework achieves higher security, reliability, and resource utilization efficiency than benchmark blockchain consensus protocols and {MEC} resource allocation algorithms.
Rakesh Saini, Abhiprada Bera, Bikash K. Behera, Emad A. Ahmed Β· 6 authors
The sixth-generation (6G) network utilizes state-of-the-art machine learning technology and obtains high attention, while the fifth-generation (5G) industry is still developing globally. Unfortunately, 6G encounters challenges to achieve performance superiority, such as scalability, massive connection, integrity, and trust. As a result, future network technologies are migrating away from centralized management entities and toward decentralized and distributed ledger technology, such as blockchain. However, the security of the blockchain is based on the computational complexity of solving specific mathematical problems that are impossible to solve on existing computers in real-time. On the other hand, quantum computers can effortlessly translate such problems with easy decryption. As a result, this study presents an architecture demonstrating the integration of quantum blockchain (QBC) with 6G networks. To show the quantum advantage, highly entangled/secured QBC of 5-, 6-, and 7-qubits are used to create the above systemβs quantum circuits. After circuit optimization, mitigation is executed with the efficiency analysis to show the advantage of the error mitigation approach in recreating the state of the QBC circuit and executing on quantum hardware. Furthermore, quantum algorithms of blockchain smart provenience contracts for the cloud-centric Internet of Things (IoT) are proposed, and corresponding quantum circuits are designed. The possible outcomes from these circuits based on the input transaction information are verified.