We propose a neural-network variational quantum algorithm to simulate the time evolution of quantum many-body systems. Based on a modified restricted Boltzmann machine (RBM) wave function ansatz, the proposed algorithm can be efficiently implemented in near-term quantum computers with low measurement cost. Using a qubit recycling strategy, only one ancilla qubit is required to represent all the hidden spins in an RBM architecture. The variational algorithm is extended to open quantum systems by employing a stochastic Schrödinger equation approach. Numerical simulations of spin-lattice models demonstrate that our algorithm is capable of capturing the dynamics of closed and open quantum many-body systems with high accuracy without suffering from the vanishing gradient (or “barren plateau”) issue for the considered system sizes.

中文翻译：

---

用于模拟多体动力学的神经网络变分量子算法

我们提出了一种神经网络变分量子算法来模拟量子多体系统的时间演化。基于改进的受限玻尔兹曼机（RBM）波函数ansatz，该算法可在近期的量子计算机中以较低的测量成本有效地实现。使用量子比特回收策略，只需一个辅助量子比特即可表示RBM架构中的所有隐藏自旋。变分算法通过采用随机Schrödinger方程方法扩展到开放式量子系统。自旋晶格模型的数值模拟表明，我们的算法能够以较高的精度捕获封闭和开放量子多体系统的动力学，而不会受到考虑的系统尺寸消失的梯度（或“贫瘠高原”）问题的影响。