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Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model
- Publication Year :
- 2022
- Publisher :
- IOP Publishing, 2022.
-
Abstract
- In this work, the capability of restricted Boltzmann machines (RBMs) to find solutions for the Kitaev honeycomb model with periodic boundary conditions is investigated. The measured groundstate (GS) energy of the system is compared and, for small lattice sizes (e.g. $3 \times 3$ with $18$ spinors), shown to agree with the analytically derived value of the energy up to a deviation of $0.09\%$. Moreover, the wave-functions we find have $99.89\%$ overlap with the exact ground state wave-functions. Furthermore, the possibility of realizing anyons in the RBM is discussed and an algorithm is given to build these anyonic excitations and braid them for possible future applications in quantum computation. Using the correspondence between topological field theories in (2+1)d and 2d CFTs, we propose an identification between our RBM states with the Moore-Read state and conformal blocks of the $2$d Ising model.<br />Comment: (15+5) pages, 11 figures, 5 tables: Minor journal revisions and additions. + Journal ref
- Subjects :
- High Energy Physics - Theory
Paper
Focus on Machine Learning for Quantum Physics
conformal blocks
Lattice (group)
Boltzmann machine
FOS: Physical sciences
Topological quantum computer
Theoretical physics
Condensed Matter - Strongly Correlated Electrons
topological field theory
46 Information and Computing Sciences
Artificial Intelligence
4611 Machine Learning
Periodic boundary conditions
4601 Applied Computing
Quantum computer
Physics
Kitaev honeycomb model
Strongly Correlated Electrons (cond-mat.str-el)
Disordered Systems and Neural Networks (cond-mat.dis-nn)
Condensed Matter - Disordered Systems and Neural Networks
Human-Computer Interaction
machine learning
High Energy Physics - Theory (hep-th)
Excited state
Ising model
Ground state
Software
restricted Boltzmann machine
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....2638bf41bd608b9a2652d45f9f5e0e80
- Full Text :
- https://doi.org/10.17863/cam.79433