Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.
Quantum optics is regarded as the acknowledged method to provide network quantum keys distribution and in the future secure distributed quantum computing, but it should also provide cryptography protection for mobile robots and the Internet of Things (IoT). This task requires the design of new secret coding schemes, which can be also based on multiple-valued logic (MVL). However, this very specific logic model reveals new possibilities for the hierarchical data clustering of arbitrary data sets. The minimization of multiple-valued logic functions is proposed for the analysis of aggregated objects, which is possible for an arbitrary number of variables. In order to use all the useful properties of the multiple-valued logic, the heterogeneous network architecture is proposed, which includes three allocated levels of artificial intelligence (AI) logic modeling for discrete multiple-valued logic, Boolean logic, and fuzzy logic. Multiple-valued logic is regarded as the possible platform for additional secret coding, data aggregation, and communications, which are provided by the united high dimensional space for network addressing and the targeted control of robotic devices. Models of Boolean and fuzzy logic are regarded as separate logic levels in order to simplify the integration of various algorithms and provide control of additional data protection means for robotic agents.
Multiple-valued logic (MVL) formalism is shown to be an effective mean to work with secret keys and to check data modification in an optical data transfer channel. Such wireless data lines are needed for multiagent robotic systems including a group of mobile robots. In order to generate secret keys by means of open optical line one can choose beforehand an arbitrary MVL function, calculate its minimized logic term and afterwards calculate a huge number of different fragments of MVL function to realize secret MVL keys. The proposed method enlarges and complements the earlier proposed effective method of MVL secret coding tables for systems with high (more than 50) number of logic levels. Some problems of correct MVL function generation are discussed. MVL check functions method is also proposed to indicate data modification in messages. MVL non-quantum coding methods can be integrated with low-throughput quantum optics data channels for additional crypto protection.
A cluster of interconnected devices able to communicate with each other is denoted as the Internet of Things (IoT). These devices can communicate with each other and exchange data over the Internet without human intervention. A range of daily usable devices, such as wearable devices, smartphones, and home appliances, are programmed in the IoT with sensors and industrial machines in various industries. The proliferation of the IoT is to collect and exchange data for task automation, improving efficiency, and providing new services. Quantum computing incorporates quantum-mechanical phenomena to perform operations on data, allowing for much faster computation than classical computers in certain tasks. This chapter will summarize a concrete idea in the field of artificial intelligence (AI); quantum computing has the qualities of significantly accelerating AI algorithms, particularly in areas such as machine learning, optimization, and simulation. With the integration of the latest techniques, quantum AI will address and solve the complex problems in many applications, such as healthcare, security, and industrial IoT.
The rapid evolution of communication networks, driven by the expansion of heterogeneous environments such as 6G, Internet of Things (IoT), and edge computing, has exposed a critical research gap in the lack of unified frameworks that jointly address intelligent network control and quantum-resilient security. Existing networking protocols were originally designed under static configurations and classical security assumptions, making them increasingly inadequate for dynamic, large-scale, and intelligent infrastructures exposed to quantum-enabled threats. At the same time, the emergence of Quantum Computing (QC) introduces severe security risks, as widely used cryptographic mechanisms supporting protocols such as Transport Layer Security (TLS) and Internet Protocol Security (IPsec) become vulnerable to quantum attacks. This paper presents a structured survey based on a systematic classification of recent literature across AI-enabled networking, Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and protocol-level integration approaches. The review categorizes existing works into AI-driven network optimization, quantum-resilient security mechanisms, and hybrid AI–QRC architectural frameworks, enabling a unified comparison of design strategies and deployment models. Furthermore, this survey analyzes how Artificial Intelligence (AI) techniques enable autonomous network operations while Quantum-Resilient Cryptography (QRC) approaches provide post-quantum security guarantees, highlighting their complementary roles in next-generation network design. The study further synthesizes key findings for a tightly coupled AI–QRC co-design, which remains an open challenge due to mismatched assumptions in control latency, cryptographic overhead, and protocol adaptability. Additionally, this review emphasizes the need for integrated frameworks that combine intelligence and quantum security to develop adaptive, scalable, and resilient network protocols for the post-quantum era.
We discuss cryptographic data protection network schemes combining the approaches of quantum cryptography with advanced computer information processing methods. The use of the latter is due to the presence of a number of unsolved problems in the existing quantum key distribution systems and is also associated with projects for the development of network-centric quantum communications and photonic networks, which involves the employment of multi-agent control models. Developments based on the Y-00 protocol with data encoding by the quantum noise of a transmitting laser are considered, and achievements in the field of quantum-optical random number generators are presented. The latter can be also used to design secure multi-valued logic encoding schemes that are promising for increasing the dimension of the key space in the 'one-time pad' method, for solving the tasks of position-dependent cryptography and for implementing multi-agent models.
With the ever-increasing proliferation of Internet of Things (IoT) devices and advent in quantum computing, the need for quantum-secure cryptographic protocols is becoming more and more critical. This paper introduces a transformative approach to Quantum Key Distribution (QKD), integrating Quantum Neural Networks (QNNs) and adaptive entanglement protocols for securing, scaling, and rendering IoT communications resilient. The proposed architecture is scalable, hardware-efficient, and noise-resilient, enabling AI-assisted protocol optimization, dynamic qubit allocation, and QNN-based noise mitigation. Simulations with Qiskit and PennyLane produce a fidelity of 99.9% and achieve an average QBER of 0.015, representing a 32% reduction compared to the 0.022 QBER typically reported for Measurement-Device-Independent-QKD (MDI-QKD). The proposed QNN model classifies with an accuracy of 97.5%, significantly outperforming the VQNN’s 80.5%. A hierarchical quantum authentication scheme is also proposed to ensure multilayer security in heterogeneous IoT settings.
Distributed cyber-infrastructures and Artificial Intelligence (AI) are transformative technologies that will play a pivotal role in the future of society and the scientific community. Internet of Things (IoT) applications harbor vast quantities of connected devices that collect a massive amount of sensitive information (e.g., medical, financial), which is usually analyzed either at the edge or federated cloud systems via AI/Machine Learning (ML) algorithms to make critical decisions (e.g., diagnosis). It is of paramount importance to ensure the security, privacy, and trustworthiness of data collection, analysis, and decision-making processes. However, system complexity and increased attack surfaces make these applications vulnerable to system breaches, single-point of failures, and various cyber-attacks. Moreover, the advances in quantum computing exacerbate the security and privacy challenges. That is, emerging quantum computers can break conventional cryptographic systems that offer cyber-security services, public key infrastructures, and privacy-enhancing technologies. Therefore, there is a vital need for new cyber-security paradigms that can address the resiliency, long-term security, and efficiency requirements of distributed cyber infrastructures.In this work, we propose a vision of distributed architecture and cyber-security framework that uniquely synergizes secure computation, Physical Quantum Key Distribution (PQKD), NIST Post- Quantum Cryptography (PQC) efforts, and AI/ML algorithms to achieve breach-resilient, functional and efficient cyber-security services. At the heart of our proposal lies a new Multi-Party Computation Quantum Network Core (MPC-QNC) that enables fast and yet quantum-safe execution of distributed computation protocols via integration of PQKD infrastructure and hardware- acceleration elements. We showcase the capabilities of MPC- QNC by instantiating it for Public Key Infrastructures (PKI) and federated ML in our HDQPKI and TPQ-ML, frameworks, respectively. HDQPKI (to the best of our knowledge) is the first hybrid and distributed post-quantum PKI that harnesses PQKD and NIST PQC standards to offer the highest level of quantum safety with a breach-resiliency against active adversaries. TPQ-ML presents a post-quantum secure and privacy-preserving federated ML infrastructure.