Quantum algorithms and simulations often require the preparation of complex states through sequences of 2-qubit gates. For a generic quantum state, the number of required gates grows exponentially with the number of qubits, becoming unfeasible on near-term quantum devices. Here, we aim at creating an approximate encoding of the target state using a limited number of gates. As a first step, we consider a quantum state that is efficiently represented classically, such as a one-dimensional matrix product state. Using tensor network techniques, we develop and implement an efficient optimization algorithm that approaches the optimal implementation, requiring a polynomial number of iterations. We, next, consider the implementation of the proposed optimization algorithm directly on a quantum computer and overcome inherent barren plateaus by employing a local cost function. Our work offers a universal method to prepare target states using local gates and represents a significant improvement over known strategies.

量子算法和模拟通常需要通过 2 量子位门序列准备复杂状态。对于一般的量子态，所需的门的数量随着量子位的数量呈指数增长，这在近期的量子设备上变得不可行。在这里，我们的目标是使用有限数量的门创建目标状态的近似编码。第一步，我们考虑经典有效表示的量子态，例如一维矩阵乘积态。使用张量网络技术，我们开发并实现了一种有效的优化算法，该算法接近最佳实现，需要多项式迭代。接下来，我们考虑直接在量子计算机上实现所提出的优化算法，并通过采用局部成本函数来克服固有的贫瘠平台。我们的工作提供了一种使用本地门准备目标状态的通用方法，并且代表了对已知策略的重大改进。