Constructing quantum circuits for efficient state preparation belongs to the central topics in the field of quantum information and computation. As the number of qubits grows fast, methods to derive large-scale quantum circuits are strongly desired. In this work, we propose the automatically differentiable quantum circuit (ADQC) approach to efficiently prepare arbitrary quantum many-qubit states. A key ingredient is to introduce the latent gates whose decompositions give the unitary gates that form the quantum circuit. The circuit is optimized by updating the latent gates using back propagation to minimize the distance between the evolved and target states. Taking the ground states of quantum lattice models and random matrix product states as examples, with the number of qubits where processing the full coefficients is unlikely, ADQC obtains high fidelities with small numbers of layers $N_L \sim O(1)$. Superior accuracy is reached compared with the existing state-preparation approach based on the matrix product disentangler. The parameter complexity of MPS can be significantly reduced by ADQC with the compression ratio $r \sim O(10^{-3})$. Our work sheds light on the "intelligent construction" of quantum circuits for many-qubit systems by combining with the machine learning methods.

翻译：用于高效状态准备的构建量子电路属于量子信息和计算领域的中心主题。 随着qubit数量快速增长, 获取大型量子电路的方法非常理想。 在这项工作中, 我们提议了自动差异量子电路( ADQC) 方法, 以高效制备任意量子多Qit状态。 一个关键要素是引入潜在门, 其分解使统一门形成量子电路。 电路通过更新潜在门, 利用回传来将进化状态和目标状态之间的距离缩小到最小值。 以量子阵模型和随机矩阵产品的地面状态为例, 以及不可能处理全部系数的qubit数量为例, ADQC 获得少量量子量子的高度匹配性。 与基于矩阵产品分解的当前状态网门相比, 达到了很高的准确性。 MPS的参数复杂性可以通过 ADQC 大幅降低, 将 $r\ sim O( )-3) 和随机矩阵产品产品 的压缩比值作为示例。 将机器的光路路段与多种光学“ ” 。