VQE example for the Toric Code Model #55
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This module implements VQE optimization for finding ground states of the generalized 2D toric code Hamiltonian with open boundary conditions and provides comparison among different ansätze: FLDC, GLDC, and FDC.
refraction-ray
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Nov 6, 2025
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In general looks good, but some changes are required as comments |
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* Standardize import order * Fix errors in code comments * Update docstrings to conform to reStructuredText (reST) style * Replace ansatz block gate sequence with a single SU(4) gate * Consolidate training functions for the three ansatzes * Fix inefficient structure where Hamiltonian (H) was rebuilt every iteration * H is now constructed only once per magnetic field strength
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Nov 8, 2025
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In general looks good to me, only some minor changes required. BTW, are the results the same after replacing with the SU(4) gates?
examples/vqe_toric.py
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| The Hamiltonian is: H = - (1 - h) \sum A_v - (1 - h) \sum B_p - h \sum (hx * X_i + hz * Z_i) | ||
| where A_v are vertex operators (X products) and B_p are plaquette operators (Z products). | ||
| Plaquette Operators at Boundaries: |
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what about vertex operators? plaquette operator actually has no boundary terms?
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There are no boundary terms for plaquette operators. In this lattice definition, every plaquette, including those at the physical boundary, is surrounded by exactly four edges (top, bottom, left, right).
…tion for building Hamiltionian.
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This module implements VQE optimization for finding ground states of the generalized 2D toric code Hamiltonian with open boundary conditions and provides comparison among different ansätze: FLDC, GLDC, and FDC. The new example demonstrates how to build the model, configure ansätze, run optimization, and compare energies and convergence behavior.