Supervised Training of Neural-Network Quantum States for the Next Nearest Neighbor Ising model
arXiv:2305.03394 · doi:10.1016/j.cpc.2024.109169
Abstract
Different neural network architectures can be unsupervisedly or supervisedly trained to represent quantum states. We explore and compare different strategies for the supervised training of feed forward neural network quantum states. We empirically and comparatively evaluate the performance of feed forward neural network quantum states in different phases of matter for variants of the architecture, for different hyper-parameters, and for two different loss functions, to which we refer as \emph{mean-squared error} and \emph{overlap}, respectively. We consider the next-nearest neighbor Ising model for the diversity of its phases and focus on its paramagnetic, ferromagnetic, and pair-antiferromagnetic phases. We observe that the overlap loss function allows better training of the model across all phases, provided a rescaling of the neural network.
References in corpus (42)
- Quantum Machine Learning
- The density-matrix renormalization group in the age of matrix product states
- Machine learning and the physical sciences
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Machine learning phases of matter
- A high-bias, low-variance introduction to Machine Learning for physicists
- Learning phase transitions by confusion
- Discovering Phase Transitions with Unsupervised Learning
- Reconstructing quantum states with generative models
- Neural-Network Approach to Dissipative Quantum Many-Body Dynamics
- Unsupervised Generative Modeling Using Matrix Product States
- Symmetries and many-body excited states with neural-network quantum states
- Efficient tomography of a quantum many-body system
- Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods
- Quantum many-body dynamics in two dimensions with artificial neural networks
- Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems
- Variational neural network ansatz for steady states in open quantum systems
- Approximating quantum many-body wave-functions using artificial neural networks
- Constructing neural stationary states for open quantum many-body systems
- Learning a local Hamiltonian from local measurements
- Neural Quantum States of frustrated magnets: generalization and sign structure
- NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
- Coupling strength estimation for spin chains despite restricted access
- Classical variational simulation of the Quantum Approximate Optimization Algorithm
- Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
- Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
- Helping restricted Boltzmann machines with quantum-state representation by restoring symmetry
- Super-resolving the Ising model with convolutional neural networks
- Neural-network quantum state tomography
- Smallest Neural Network to Learn the Ising Criticality
- Transfer learning for scalability of neural-network quantum states
- Learning the dynamics of open quantum systems from their steady states
- Tensor network based machine learning of non-Markovian quantum processes
- Learning the Ising Model with Generative Neural Networks
- Neural network state estimation for full quantum state tomography
- Variational classical networks for dynamics in interacting quantum matter
- Analysis of a density matrix renormalization group approach for transport in open quantum systems
- Ground state search by local and sequential updates of neural network quantum states
- Typicality of nonequilibrium (quasi-)steady currents
- Emergence of steady currents due to strong prethermalization
- Deep learning Local Reduced Density Matrices for Many-body Hamiltonian Estimation
- Machine Learning approach to the Floquet--Lindbladian problem