Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose variational quantum optimization algorithm (VQOA), which leverages many-qubit (MQ) operations in an ansatz solely employing quantum superposition, completely avoiding entanglement. This ansatz significantly reduces circuit complexity, enhances noise robustness, mitigates Barren Plateau issues, and enables efficient partitioning for highly complex large-scale optimization. Furthermore, we introduce distributed VQOA (DVQOA), which integrates high-performance computing with quantum computing to achieve superior performance across MQ systems and classical nodes. These features enable a significant acceleration of material optimization tasks (e. g. , metamaterial design), achieving more than 50 speedup compared to state-of-the-art optimization algorithms. Additionally, DVQOA efficiently solves quantum chemistry problems and N-ary (N 2) optimization problems involving higher-order interactions. These advantages establish DVQOA as a highly promising and versatile solver for real-world problems, demonstrating the practical utility of the quantum-classical approach.
Kim et al. (Fri,) studied this question.