标题： 具有多量子比特的分布式变分量子优化算法挑战 显示英文标题

标题： Distributed Variational Quantum Algorithm with Many-qubit for Optimization Challenges

摘要： 优化问题在各个领域都至关重要，然而现有的量子算法尽管潜力巨大，但由于过度依赖纠缠，在可扩展性和准确性方面仍面临挑战。 为了解决这些局限性，我们提出了变分量子优化算法（VQOA），该算法利用了基于量子叠加的变分形式中的多量子比特（MQ）操作，完全避免了纠缠的使用。 这种变分形式显著降低了电路复杂度，增强了抗噪能力，缓解了“荒芜高原”问题，并使高度复杂的大型优化问题能够高效分解。 此外，我们引入了分布式变分量子优化算法（DVQOA），它结合高性能计算与量子计算，以实现多量子比特系统和经典节点上的卓越性能。 这些特性使得材料优化任务（例如超材料设计）得到了显著加速，相比最先进的优化算法，速度提升了超过 50 倍$\times$。 此外，DVQOA 高效解决了量子化学问题以及涉及高阶相互作用的$\textit{N}$元$(N \geq 2)$优化问题。 这些优势确立了 DVQOA 作为解决实际问题的高度有前景且多功能的求解器，展示了量子-经典方法的实际实用性。 显示英文摘要

摘要： 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$\times$ speedup compared to state-of-the-art optimization algorithms. Additionally, DVQOA efficiently solves quantum chemistry problems and $\textit{N}$-ary $(N \geq 2)$ 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.