Restricted Boltzmann Machines and Matrix Product States of 1D Translational Invariant Stabilizer Codes
arXiv:1812.08171 · doi:10.1103/PhysRevB.99.155129
Abstract
We discuss the relations between restricted Boltzmann machine (RBM) states and the matrix product states (MPS) for the ground states of 1D translational invariant stabilizer codes. A generic translational invariant and finitely connected RBM state can be expressed as an MPS, and the matrices of the resulting MPS are of rank 1. We dub such an MPS as an RBM-MPS. This provides a necessary condition for exactly realizing a quantum state as an RBM state, if the quantum state can be written as an MPS. For generic 1D stabilizer codes having a non-degenerate ground state with periodic boundary condition, we obtain an expression for the lower bound of their MPS bond dimension, and an upper bound for the rank of their MPS matrices. In terms of RBM, we provide an algorithm to derive the RBM for the cocycle Hamiltonians whose MPS matrices are proved to be of rank 1. Moreover, the RBM-MPS produced by our algorithm has the minimal bond dimension. A family of examples is provided to explain the algorithm. We finally conjecture that these features hold true for all the 1D stabilizer codes having a non-degenerate ground state with periodic boundary condition, as long as their MPS matrices are of rank 1.
arXiv comment36 pages, 16 figures
References in corpus (32)
- Fault-tolerant quantum computation by anyons
- The density-matrix renormalization group in the age of matrix product states
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- Matrix Product States, Projected Entangled Pair States, and variational renormalization group methods for quantum spin systems
- Symmetry protected topological orders and the group cohomology of their symmetry group
- Thermodynamic limit of the density matrix renormalization for the spin-1 Heisenberg chain
- An Area Law for One Dimensional Quantum Systems
- Many-body quantum state tomography with neural networks
- Entropy scaling and simulability by Matrix Product States
- Quantum Entanglement in Neural Network States
- Efficient Representation of Quantum Many-body States with Deep Neural Networks
- Restricted-Boltzmann-Machine Learning for Solving Strongly Correlated Quantum Systems
- Machine Learning Topological States
- Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
- Equivalence of restricted Boltzmann machines and tensor network states
- Twisted Quantum Double Model of Topological Phases in Two--Dimension
- Approximating quantum many-body wave-functions using artificial neural networks
- Neural-Network Quantum States, String-Bond States, and Chiral Topological States
- Field theory representation of gauge-gravity symmetry-protected topological invariants, group cohomology and beyond
- Constructing exact representations of quantum many-body systems with deep neural networks
- Emergence of Compositional Representations in Restricted Boltzmann Machines
- Twisted Gauge Theory Model of Topological Phases in Three Dimensions
- Topological Entanglement Entropy of Fracton Stabilizer Codes
- Quantum Neural Network States: A Brief Review of Methods and Applications
- Chiral Topological Phases from Artificial Neural Networks
- Entanglement Entropy From Tensor Network States for Stabilizer Codes
- Spurious Long-range Entanglement and Replica Correlation Length
- Unifying Neural-network Quantum States and Correlator Product States via Tensor Networks
- Rokhsar-Kivelson Models of Bosonic Symmetry-Protected Topological States
- Efficient Machine Learning Representations of Surface Code with Boundaries, Defects, Domain Walls and Twists
- Restricted Boltzmann Machines for the Long Range Ising Models
- Identifying Product Order with Restricted Boltzmann Machines
Cited by in corpus (15)
- Machine learning and the physical sciences
- Restricted Boltzmann Machines for Quantum States with Nonabelian or Anyonic Symmetries
- QuCumber: wavefunction reconstruction with neural networks
- A perspective on machine learning and data science for strongly correlated electron problems
- Approximating power of machine-learning ansatz for quantum many-body states
- Tensor networks for interpretable and efficient quantum-inspired machine learning
- Simulating disordered quantum systems via dense and sparse restricted Boltzmann machines
- Compact Neural-network Quantum State representations of Jastrow and Stabilizer states
- Neural-network Quantum States for Spin-1 systems: spin-basis and parameterization effects on compactness of representations
- Specialising Neural-network Quantum States for the Bose Hubbard Model
- Non-stabilizerness of Neural Quantum States
- Efficiency of neural-network state representations of one-dimensional quantum spin systems
- Seeding neural network quantum states with tensor network states
- Worldline algorithm by oracle-guided variational autoregressive network
- Group Convolutional Neural Network for the Low-Energy Spectrum in the Quantum Dimer Model