Variational Learning of Quantum Ground States on Spiking Neuromorphic Hardware
Variational Learning of Quantum Ground States on Spiking Neuromorphic Hardware
Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical-model devices offer a fast, efficient and inherently parallel substrate capable of related …