Quantum-Classical Hybrid Genetic Evolutionary Algorithm for Topology Optimization of Continuum Structures

Quantum computing platforms offer unique advantages—such as inherent parallelism and efficient handling of large-scale computations—that unlock novel solutions for complex structural design challenges. This paper introduces QCHGEA-TOF (Quantum-Classical Hybrid Genetic Evolutionary Algorithm-Based Topology Optimization Framework), a method that integrates quantum computing to enhance global search capabilities. The framework maps structural elements to qubits in quantum circuits, enabling efficient exploration of design configurations through quantum superposition and parallelism. Classical computing components employ finite element analysis, image processing strategies, and bidirectional evolutionary structural optimization (BESO) to ensure structural feasibility, connectivity, and precision. Benchmark case studies demonstrate that QCHGEA-TOF achieves lower structural compliance compared to traditional algorithms like GA and BESO, highlighting its potential for generating high-quality optimized topologies. These results underscore QCHGEA-TOF's ability to address complex global optimization challenges in structural design. Future research will focus on quantifying its computational efficiency and scalability, paving the way for broader applications of quantum-classical hybrid methods in topology optimization.