Representations of hypergraph states with neural networks*
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Publication:6046360
DOI10.1088/1572-9494/ac1101zbMath1515.82123MaRDI QIDQ6046360
Publication date: 10 May 2023
Published in: Communications in Theoretical Physics (Search for Journal in Brave)
Many-body theory; quantum Hall effect (81V70) Neural nets applied to problems in time-dependent statistical mechanics (82C32)
Cites Work
- The density-matrix renormalization group in the age of matrix product states
- Multilayer feedforward networks are universal approximators
- Reducing the Dimensionality of Data with Neural Networks
- Entropy Scaling and Simulability by Matrix Product States
- Computational Complexity of Projected Entangled Pair States
- Criticality, the Area Law, and the Computational Power of Projected Entangled Pair States
- Representational Power of Restricted Boltzmann Machines and Deep Belief Networks
- Solving the quantum many-body problem with artificial neural networks
- Quantum hypergraph states
- On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition
- Approximation by superpositions of a sigmoidal function
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