论文标题
迈向变变量子算法的神经网络模拟
Toward Neural Network Simulation of Variational Quantum Algorithms
论文作者
论文摘要
变性量子算法（VQAS）利用混合量子古典体系结构将高维线性代数的重铸问题作为随机优化的问题。尽管有望利用接近到中期量子资源来加速这项任务，但VQA的计算优势比完全古典算法的计算优势尚未确定。例如，虽然已开发出变异量子量化量化（VQE），以近似高维稀疏线性运算符的低较低特征模量，但在变量蒙特卡洛（VMC）文献中存在类似的经典优化算法，利用神经网络，利用量子循环来代表量子循环。在本文中，我们询问是否可以构造经典的随机优化算法与其他VQA并行，重点是变异量子线性求解器（VQLS）的示例。我们发现，这种结构可以应用于VQL，从理论上讲可以扩展到其他类似形式的VQA，从而产生范式。
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization. Despite the promise of leveraging near- to intermediate-term quantum resources to accelerate this task, the computational advantage of VQAs over wholly classical algorithms has not been firmly established. For instance, while the variational quantum eigensolver (VQE) has been developed to approximate low-lying eigenmodes of high-dimensional sparse linear operators, analogous classical optimization algorithms exist in the variational Monte Carlo (VMC) literature, utilizing neural networks in place of quantum circuits to represent quantum states. In this paper we ask if classical stochastic optimization algorithms can be constructed paralleling other VQAs, focusing on the example of the variational quantum linear solver (VQLS). We find that such a construction can be applied to the VQLS, yielding a paradigm that could theoretically extend to other VQAs of similar form.
