We train a neuromorphic hardware chip to approximate the ground states of quantum spin models by variational energy minimization. Compared to variational artificial neural networks using Markov chain Monte Carlo for sample generation, this approach has the advantage that the neuromorphic device generates samples in a fast and inherently parallel fashion. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes ($N\leq 10$). A systematic hyperparameter study shows that scalability to larger system sizes mainly depends on sample quality which is limited by parameter drifts on the analog neuromorphic chip. The learning performance shows a threshold behavior as a function of the number of variational parameters of the ansatz, with approximately $50$ hidden neurons being sufficient for representing critical ground states up to $N=10$. The 6+1-bit resolution of the network parameters does not limit the reachable approximation quality in the current setup. Our work provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.

翻译：我们训练了一个神经形态硬件芯片,以通过变量能量最小化来接近量子旋转模型的地面状态。与利用Markov链链Monte Carlo进行样本生成的变形人工神经网络相比,这种方法的优点是神经形态装置以快速和内在的平行方式生成样本。我们开发了一种培训算法并将其应用到横田Ising模型,显示中等系统大小(N\leq 10美元)的良好性能。一个系统的超参数研究表明,向较大系统大小的缩放主要取决于样本质量,而样本质量因模拟神经形态芯的参数漂移而受到限制。学习性能显示一个临界值行为,这是ansatz变异参数数的函数,大约50美元隐藏的神经元足以代表临界地面状态达10美元。网络参数的6+1位分辨率并不限制当前设置的可达近似近距离质量。我们的工作为利用神经形态硬件处理数量众多身体问题的临界值的能力迈出了重要的一步。