Accept MajoranaSparse in MajoranaEncoding.encode - #216
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The Majorana representation was already the internal encoding path: encode() converted a FermionHamiltonian via to_majorana_sparse() and dispatched to Encode<&MajoranaSparse, QubitHamiltonian>. Callers already holding a MajoranaSparse had to round-trip through a FermionHamiltonian to reach it. encode() now takes either type. Python has no overload resolution and #[pymethods] cannot expose a generic method, so the two inputs are dispatched at runtime through an untagged FromPyObject enum. Both already produce a QubitHamiltonian, so the signature stays uniform. MajoranaSparse carries no mode count and the core encode indexes `operators` unguarded, so out-of-range indices are rejected up front rather than panicking inside a rayon worker. Terms are ordered lexicographically rather than by magnitude, so the whole index set is scanned. The encode parameter is renamed fham -> operator, as it is no longer restricted to a FermionHamiltonian. TernaryTree.encode_naive is widened to match. Bumps the version to 0.13.0. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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Encodes a single product of Majorana operators, the Majorana counterpart of encode_fermion_product. Majorana operators are hermitian, so unlike the ladder-operator case the product is described by its indices alone and takes no signature. Operators are multiplied in the order given rather than canonicalised: the symplectic product already tracks the phase from each multiplication, so reordering would silently change the caller's operator. Indices are bounded by 2 * n_modes rather than n_modes, and out-of-range values are rejected up front to keep them away from the unguarded row indexing in the core encode. Exposed directly as a pymethod rather than split into a private _impl helper, as encode_product_impl is, since it has a single caller. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
…Tree A Majorana product always encodes to exactly one Pauli term, so wrapping it in a QubitHamiltonian made callers unpack a one-entry map. Return the (pauli_string, coefficient) pair directly instead. Also adds TernaryTree.encode_majorana_product, which builds the encoding on demand, so callers no longer reach into the private _encoding attribute to get at it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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* Consolidate to_sparse_majorana into to_majorana_sparse 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> * Accept MajoranaSparse in remaining FermionHamiltonian-only encoding methods 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> * 0.13.1 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> --------- Co-authored-by: Claude Haiku 4.5 <noreply@anthropic.com>
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Adds pyo3 wrapper to allow passing existing PyMajoranaSparse to PyMajoranaEncoding.