Advanced quantum computing-driven digital twin for energy and timing optimization in low-power VLSI circuits &IoT devices.

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.