The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
The proliferation of Internet of Things (IoT) devices has created a critical demand for highly energy-efficient and timing-accurate Very Large-Scale Integration (VLSI) circuits. Traditional simulation and modeling techniques often fall short in capturing real-time performance metrics, particularly under dynamic operating conditions and process variations. Digital Twin (DT) technology has emerged as a transformative approach, enabling real-time mirroring and prediction of physical systems. However, classical DT frameworks are computationally limited when simulating complex VLSI behaviors involving multiple non-linear interactions and stringent power constraints. To address these challenges, this research proposes a novel quantum computing-enhanced digital twin framework designed for low-power VLSI circuits in IoT applications. The proposed system leverages the computational parallelism and probabilistic modeling capabilities of quantum algorithms to accelerate the simulation and optimization of power and timing metrics. A hybrid quantum-classical architecture is employed, where a quantum variational circuit models the probabilistic switching behavior of logic gates, while classical components handle data acquisition and control operations. Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are integrated to minimize energy dissipation and optimize timing paths, respectively. Real-time telemetry from IoT hardware is used to continuously update the twin, ensuring fidelity with the physical circuit. Experimental evaluations on industry-standard benchmark circuits (e.g., ISCAS-85, ITC-99) demonstrate significant improvements in dynamic power estimation accuracy and critical path timing prediction when compared to classical simulation methods. Energy consumption was reduced by up to 17%, and timing deviations were minimized by 22% under varying operating conditions. Moreover, the quantum-DT framework proved robust in the presence of noise and variations, exhibiting scalability for large-scale circuit designs. This study highlights the potential of integrating quantum computing with digital twin technology to meet the stringent design goals of modern VLSI systems, especially in resource-constrained IoT environments. The proposed framework not only enhances modeling precision but also enables real-time adaptive optimization, paving the way for next-generation intelligent circuit design and autonomous system calibration.
The growing complexity of Very Large Scale Integration (VLSI) circuit designs has significantly increased the demand for innovative approaches to enhance design efficiency and fabrication accuracy. Traditional computational methods face limitations in terms of scalability and optimization for complex VLSI systems. The advent of quantum computing presents a promising paradigm shift, offering exponential speedup in solving computationally intensive problems such as optimization, simulation, and data analysis in VLSI design and fabrication. This research investigates the potential of quantum computing algorithms to optimize VLSI circuit design and fabrication processes, addressing key challenges such as layout optimization, routing, and process variation. The problem lies in the increasing complexity and size of VLSI circuits, which often lead to inefficient designs, longer manufacturing times, and increased cost. Current methods such as classical algorithms and heuristic techniques struggle to achieve optimal solutions within reasonable time frames. Quantum algorithms, particularly those based on quantum annealing and variational quantum eigensolvers (VQE), have shown promise in solving combinatorial optimization problems with much higher efficiency. By leveraging quantum computing, the goal is to improve the optimization of circuit layouts, minimize power consumption, and reduce production costs in VLSI systems. The proposed method utilizes quantum computing techniques to model and solve optimization problems related to VLSI circuit design and fabrication. Simulations and comparisons with classical methods show significant improvements in design efficiency, reduced error rates, and faster optimization cycles. The outcomes of this study could transform the future of VLSI design and fabrication, leading to more efficient, cost-effective, and scalable solutions for semiconductor manufacturing.
Abstract Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.
Innovative optimization methods that balance power efficiency, computational complexity, and performance are needed to meet IoT device demand for low-power, high-performance VLSI circuits. Combining quantum computing with traditional optimization methods is necessary due to the exponential development of circuit complexity. This study optimizes low-power VLSI circuits using a hybrid quantum-classical framework that uses quantum circuit optimization, quantum annealing, and hybrid variational methods. The framework reduces gate count, power consumption, and latency by combining quantum-assisted logic optimization with classical heuristics. Simulations and experiments prove the framework can improve energy-efficient hardware designs. Quantum gate reduction (QGR) and quantum state encoding (QSE) optimize Boolean logic, while quantum annealing refines transistor location and reduces leakage power. Classical post-processing methods like heuristic refinement and logic gate remapping fine-tune quantum-optimized circuits for practical application. Compared to classical approaches, the hybrid architecture reduces gate count by 35%, saves 28% power, and improves latency by 25%. Performance validation using IBM Quantum Experience and D-Wave devices shows that quantum-assisted VLSI optimization techniques are possible. Further research on fault-tolerant quantum computing, hybrid co-design, and real-world CMOS integration for next-generation semiconductor fabrication are planned. This study shows how quantum-classical hybrid techniques can alter electronic design automation.
The advent of quantum computing is redefining the computational landscape of industrial manufacturing, introducing new paradigms for simulation, optimization, and adaptive decision-making. This study investigates the integration of quantum computing–enhanced service ecosystems within industrial manufacturing frameworks, focusing on how quantum algorithms can accelerate workflow optimization, supply chain coordination, and process simulation in large-scale production environments. Traditional simulation and optimization techniques often struggle with the combinatorial complexity of modern manufacturing systems, leading to inefficiencies in resource allocation, scheduling, and predictive maintenance. The proposed framework leverages hybrid quantum–classical computation to simulate high-dimensional manufacturing workflows through quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and quantum-inspired reinforcement learning. These techniques enable more efficient exploration of multi-objective optimization spaces, facilitating near-real-time decision support and improved throughput under uncertain production conditions. The model also emphasizes the development of service-oriented architectures that integrate quantum computation with industrial Internet of Things (IIoT) infrastructures, digital twins, and cloud-based manufacturing services. Results from conceptual modeling and early simulation experiments suggest that quantum-enhanced service ecosystems outperform conventional digital twins and heuristic optimization methods in adaptability, precision, and energy efficiency. This convergence of quantum computing and industrial systems marks a shift toward next-generation intelligent manufacturing, capable of autonomous learning, dynamic reconfiguration, and sustainable production optimization.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
In the rapidly evolving landscape of Industry 4.0, industrial systems have become increasingly complex, interconnected, and data-intensive. Traditional computational methods often fall short in managing the highdimensional, nonlinear challenges presented by such environments. This paper explores a novel framework that integrates Big Data analytics with quantum computing techniques to model and optimize complex industrial systems-including Smart Grids, Supply Chains, and Manufacturing processes. By leveraging algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Quantum Machine Learning (QML), the study demonstrates substantial improvements in processing time, accuracy, and resource efficiency compared to classical methods. Empirical evaluation through a structured experimental design and statistical validation via ANOVA confirms that hybrid quantum-classical approaches outperform traditional models across multiple performance metrics. This integration paves the way for real-time, scalable, and intelligent industrial optimization, offering a transformative leap in operational decision-making, fault prediction, and strategic control.