用物理约束神经网络加速量子优化，大幅降低资源消耗。
PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
- 将量子算法训练过程建模为偏微分方程，用物理信息神经网络模拟动态。
- 40量子比特任务下速度提升30倍，量子资源消耗减少90%。
- 适合大规模量子优化问题，尤其适用于资源受限场景。
变分量子算法（VQAs）是实现近中期量子设备实用化的主要策略。然而，量子力学中的不可克隆定理禁止标准反向传播，导致在大规模任务中应用VQAs时量子资源开销巨大。为此，我们把VQAs的训练动力学重新表述为非线性偏微分方程，并提出一种新协议，利用物理信息神经网络（PINNs）高效建模该动力系统。仅需从量子设备收集少量训练轨迹数据，该协议即可在经典侧预测多轮迭代下的参数更新，显著降低量子资源成本。系统性数值实验表明，与传统方法相比，本方法在涉及最多40个量子比特的任务中实现了高达30倍的速度提升，量子资源消耗降低达90%，同时保持了竞争力的精度。该方法补充了现有提高VQA效率的技术，进一步增强了其实际应用潜力。
原文摘要 · Abstract (English)
Variational quantum algorithms (VQAs) are leading strategies to reach practical utilities of near-term quantum devices. However, the no-cloning theorem in quantum mechanics precludes standard backpropagation, leading to prohibitive quantum resource costs when applying VQAs to large-scale tasks. To address this challenge, we reformulate the training dynamics of VQAs as a nonlinear partial differential equation and propose a novel protocol that leverages physics-informed neural networks (PINNs) to model this dynamical system efficiently. Given a small amount of training trajectory data collected from quantum devices, our protocol predicts the parameter updates of VQAs over multiple iterations on the classical side, dramatically reducing quantum resource costs. Through systematic numerical experiments, we demonstrate that our method achieves up to a 30x speedup compared to conventional methods and reduces quantum resource costs by as much as 90\% for tasks involving up to 40 qubits, including ground state preparation of different quantum systems, while maintaining competitive accuracy. Our approach complements existing techniques aimed at improving the efficiency of VQAs and further strengthens their potential for practical applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。