Msparse everywhere - #217
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Remove the duplicate `to_sparse_majorana()` method and add `to_dict()` to `PyMajoranaSparse` to provide the same dict-like functionality. This simplifies the API by having a single conversion method that can return either the structured `PyMajoranaSparse` object or a dict via `.to_dict()`. - Remove `to_sparse_majorana()` from FermionHamiltonian - Add `to_dict()` method to PyMajoranaSparse that returns simplified terms - Update test callers to use `to_majorana_sparse().to_dict()` - Update comment in functions.rs to reference new method Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
…ethods Widens MajoranaEncoding.encode_annealed, .anneal_enumeration, .batch_pauli_weights, the hatt and encode_topphatt module functions, and their Python wrappers (TernaryTree.encode_annealed/.topphatt/.encode, hamiltonian_adaptive_ternary_tree) to accept a MajoranaSparse directly, matching the pattern already used by MajoranaEncoding.encode (#216). Also fixes TernaryTree.encode, whose type hint already claimed to accept MajoranaSparse but would raise a TypeError at the pyo3 boundary since encode_topphatt only accepted FermionHamiltonian. PyMajoranaSparse now carries the source Hamiltonian's n_modes, since the core MajoranaSparse type has no mode count of its own and several of these methods need one to compute defaults (temperature, n_modes). This also lets the ad-hoc index-range scan in MajoranaEncoding.encode be replaced with a direct mode-count comparison, mirroring the check already used for FermionHamiltonian inputs. Also removes the to_sparse_majorana stub left behind in core.pyi from the prior consolidation onto to_majorana_sparse, and adds the missing to_dict stub for MajoranaSparse. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
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Merging this PR will not alter performance
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Some functions forgotten in #216 , and deleting a confusingly similar
to_sparse_majoranawhich I had forgotten about .