Finding Quantum Critical Points with Neural-Network Quantum States
This work addresses the challenge of studying quantum many-body systems for physicists, but it is incremental as it applies existing neural-network methods to a specific model.
The researchers tackled the problem of locating quantum critical points in many-body systems by using neural-network quantum states, achieving efficient and effective results compared to traditional methods.
Finding the precise location of quantum critical points is of particular importance to characterise quantum many-body systems at zero temperature. However, quantum many-body systems are notoriously hard to study because the dimension of their Hilbert space increases exponentially with their size. Recently, machine learning tools known as neural-network quantum states have been shown to effectively and efficiently simulate quantum many-body systems. We present an approach to finding the quantum critical points of the quantum Ising model using neural-network quantum states, analytically constructed innate restricted Boltzmann machines, transfer learning and unsupervised learning. We validate the approach and evaluate its efficiency and effectiveness in comparison with other traditional approaches.