Increased development of high-performance computing (HPC) has increased the pressure on new paradigms in VLSI circuit design, as device scaling is approaching scaling limits where quantum effects become important.Conventional electronic design automation (EDA) processes have difficulties in dealing with nonlinear interactions that occur when quantum tunnelling, leakage currents, and probabilistic switching are also present in the deeply scaled technology.To overcome them, this paper suggests an all-encompassing AI/ML-based EDA architecture, which incorporates quantum-aware modelling, predictive synthesis, and adaptive optimization of future HPC-oriented next-generation VLSI systems.The framework has incorporated machine learning (ML)-based parametric estimation, reinforcement learning (RL) on layout exploration, and physics-guided neural models: nonclassical effects in nanoscale transistors.Furthermore, the system uses generative learning algorithms to create multiobjective design trade-offs in terms of timing, power, area, and quantum reliability.The hybrid digital-quantum design flow is presented, allowing to easily interchange the classical EDA operations and quantum-inspired device tests.Nanometer-scale benchmark circuit confirmation of the efficiency of synthesis, accuracy of leakage prediction, and rate of convergence of optimization are shown to be much enhanced with respect to more traditional EDA pipelines.The given methodology puts the emphasis on the role of intelligent automation as the means of guiding the VLSI research toward the point of quantum-awareness as the key to ensuring reliability, scalability, and energy efficiency in an HPC system.
Increased development of high-performance computing (HPC) has increased the pressure on new paradigms in VLSI circuit design, as device scaling is approaching scaling limits where quantum effects become important.Conventional electronic design automation (EDA) processes have difficulties in dealing with nonlinear interactions that occur when quantum tunnelling, leakage currents, and probabilistic switching are also present in the deeply scaled technology.To overcome them, this paper suggests an all-encompassing AI/ML-based EDA architecture, which incorporates quantum-aware modelling, predictive synthesis, and adaptive optimization of future HPC-oriented next-generation VLSI systems.The framework has incorporated machine learning (ML)-based parametric estimation, reinforcement learning (RL) on layout exploration, and physics-guided neural models: nonclassical effects in nanoscale transistors.Furthermore, the system uses generative learning algorithms to create multiobjective design trade-offs in terms of timing, power, area, and quantum reliability.The hybrid digital-quantum design flow is presented, allowing to easily interchange the classical EDA operations and quantum-inspired device tests.Nanometer-scale benchmark circuit confirmation of the efficiency of synthesis, accuracy of leakage prediction, and rate of convergence of optimization are shown to be much enhanced with respect to more traditional EDA pipelines.The given methodology puts the emphasis on the role of intelligent automation as the means of guiding the VLSI research toward the point of quantum-awareness as the key to ensuring reliability, scalability, and energy efficiency in an HPC system.
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.
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.
Use of AI/ML is associated with more and more computing power, which often necessitates the application to guzzle energy and take time to train. The paper presents a Quantum-Enhanced High-Performance Computing (Q-HPC) system that will combine traditional HPC units with quantum-assisted optimization to enhance the model training in addition to the predictive accuracy and energy efficiency. The framework that could be used to work with such large volumes of data is multi- GPU/CPU parallelization, and the optimization of parameters and hyperparameters can be implemented with the help of quantum-inspired algorithms. This will lead to a hybrid computation balancing dynamically the classical and quantum computations. Q-HPC was experimented with multiple AI/ML model types, such as convolutional networks, transformer models, graph neural networks and reinforcement learning agents, in which cases it was observed to traverse to the solution faster, more accurately and used less energy than the traditional HPC. It can also be said that the framework is dynamically adaptable and sustainable, i.e. it could be deployed to a massive diversifying range of tasks of AI/ML. The suggested model is a combination of the performance and scalability of the classical HPC with the optimization performance of quantum computing in order to create a new and valuable approach to the next generation AI. It deals with the issues of performance and environmental concerns of high-performance computing.
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.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
The convergence of quantum computing and artificial intelligence (AI) is redefining the boundaries of computational engineering, offering an unprecedented capacity for optimization, simulation, and design automation. This research introduces Quantum-Driven Computer-Aided Engineering (Q-CAE), a hybrid AI–quantum framework developed to accelerate design space exploration and enhance the precision of optimization in complex engineering systems. Q-CAE combines the analytical rigor of quantum algorithms with the adaptability of AI-driven optimization models to address the challenges of high-dimensional, nonlinear, and interdependent design parameters that traditionally strain classical computing resources. Within this framework, quantum subroutines such as Quantum Approximate Optimization Algorithms (QAOA) and Variational Quantum Circuits (VQC) are employed to efficiently search large solution spaces, while AI models leverage reinforcement learning and neural networks to predict optimal configurations and refine performance iteratively. This hybrid approach enables dynamic learning from multi-objective data sets, optimizing energy efficiency, material properties, and system reliability. Participants engaged with Q-CAE applications gained first-hand experience in algorithmic modeling, quantum heuristics, and AI-driven engineering design, cultivating interdisciplinary skills essential for future computational innovation. Results indicate that hybrid Q-CAE systems can significantly reduce computational time and enhance accuracy compared to traditional CAE workflows. The study demonstrates how integrating quantum principles into engineering design pipelines can transform modern computational methodologies, setting the foundation for autonomous, intelligent, and quantum-enhanced engineering systems.