Hybrid quantum-classical framework for optimizing low-power VLSI circuits in IoTdevices

Abstract

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.