Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems

Abstract

Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-body problems. In sharp contrast to Hamiltonian/task specific models, TQS can generate the entire phase diagram, predict field strengths with experimental measurements, and transfer such a knowledge to new systems it has never been trained on before, all within a single model. With specific tasks, fine-tuning the TQS produces accurate results with small computational cost. Versatile by design, TQS can be easily adapted to new tasks, thereby pointing towards a general-purpose model for various challenging quantum problems.

arXiv comment12 pages, 13 figures

References in corpus (21)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The density-matrix renormalization group in the age of matrix product states
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Machine learning phases of matter
• Predicting Many Properties of a Quantum System from Very Few Measurements
• Many-body quantum state tomography with neural networks
• Learning phase transitions by confusion
• Efficient numerical simulations with Tensor Networks: Tensor Network Python (TeNPy)
• Quantum Entanglement in Neural Network States
• Reconstructing quantum states with generative models
• Recurrent Neural Network Wave Functions
• Deep autoregressive models for the efficient variational simulation of many-body quantum systems
• Symmetries and many-body excited states with neural-network quantum states
• Study of the Two-Dimensional Frustrated J1-J2 Model with Neural Network Quantum States
• Weak binding between two aromatic rings: feeling the van der Waals attraction by quantum Monte Carlo methods
• Approximating quantum many-body wave-functions using artificial neural networks
• Solving frustrated quantum many-particle models with convolutional neural networks
• Topological Quantum Compiling with Reinforcement Learning
• Autoregressive Transformer Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation
• Attention-based Quantum Tomography
• Probabilistic Simulation of Quantum Circuits with the Transformer

Cited by in corpus (20)

• Variational Benchmarks for Quantum Many-Body Problems
• Variational Monte Carlo with Large Patched Transformers
• Adaptive Quantum State Tomography with Active Learning
• Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians
• Variational optimization of the amplitude of neural-network quantum many-body ground states
• Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems
• Deep learning of many-body observables and quantum information scrambling
• Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems
• Autoregressive neural quantum states of Fermi Hubbard models
• Impact of conditional modelling for a universal autoregressive quantum state
• Hybrid quantum-classical unsupervised data clustering based on the self-organizing feature map
• Learning the expressibility of quantum circuit ansatz using transformer
• Time-dependent Neural Galerkin Method for Quantum Dynamics
• Quantum feedback control with a transformer neural network architecture
• Fluctuation based interpretable analysis scheme for quantum many-body snapshots
• Physics-informed Transformers for Electronic Quantum States
• Attention to Quantum Complexity
• Neural network enhanced cross entropy benchmark for monitored circuits
• Variational Transformer Ansatz for the Density Operator of Steady States in Dissipative Quantum Many-Body Systems
• Neural network ensemble for computing cross sections for rotational transitions in H${}_2$O + H${}_2$O collisions