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SUMMARY:Extracting Anyon Statistics from Neural Network Fractional Quantum
  Hall States - Andres Perez Fadon\, Imperial College
DTSTART:20251204T140000Z
DTEND:20251204T151500Z
UID:TALK235045@talks.cam.ac.uk
CONTACT:Gaurav
DESCRIPTION:In recent years\, with the accelerated development of artifici
 al intelligence\, a growing field of computational quantum chemistry is th
 e use of neural networks as ansatze for variational Monte Carlo calculatio
 ns. This neural wave functions approach has been demonstrated to be a very
  accurate technique for obtaining numerical solutions of the Schrodinger e
 quation. I will start by giving an introduction to the field of neural-net
 work variational Monte Carlo\, followed by how we have recently adapted th
 em to study the fractional quantum Hall effect.\n\nThe fractional quantum 
 Hall effect hosts emergent anyons with exotic exchange statistics\, but di
 rect numerical access to their topological properties in the continuum has
  remained limited. Most computational approaches are restricted to a singl
 e Landau level\, which precludes treating realistic regimes with strong La
 ndau-level mixing. Using neural-network variational Monte Carlo\, we obtai
 n the 3 degenerate ground states at filling factor ν = 1/3. From these st
 ates\, we extract the modular S matrix via entanglement interferometry\, a
  technique previously applied only to lattice models. The resulting S matr
 ix encodes the quantum dimensions\, fusion rules\, and exchange statistics
  of the emergent anyons\, providing a direct numerical demonstration of th
 eir topological order. The calculated anyon properties match the well-know
 n theoretical and experimental results. Our work establishes neural-networ
 k wavefunctions as a powerful new tool for investigating anyonic propertie
 s.
LOCATION:Seminar Room 3\, RDC
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