MajoranaPropagator

Classical simulator for Majorana operators.

Accepts a MajoranaOperator (or any object implementing get_majorana_operator(), such as a FermiOperator) and a Circuit of Majorana/fermionic gates. See MonomialPropagator for the shared surface.

Attributes

attributen_gatesint

Number of authoring gates ingested into the graph.

A single-Majorana-monomial gate expands to one graph layer; a multi-monomial gate expands to several layers sharing one gate, so n_gates \<= graph_layers. Stays correct after a graph prefix is consumed by contract_partially / propagate.

attributeparameter_mappinglist[int]

The parameter mapping owned by the graph, one entry per graph layer.

Entry i is the variational-parameter index driving the i-th graph layer (a generated Majorana monomial), in the same order as the parameter vector passed to expectation_value. This is the graph's native (per-monomial) mapping, which is finer-grained than the per-gate mapping of the authoring Circuit when gates bundle several monomials.

attributegraph_layersint

Number of evolved Majorana monomials (graph layers).

attributenum_modesint

Number of fermionic modes for the simulator.

Functions

func__init__

(self, initial_operator, initial_state, *, cutoff, schrodinger_cutoff=None, cutoff_type='length', lower_atol=None, upper_atol=None, comm=None) -> None

Initialize the propagator.

paramself

paraminitial_operatorIQuantumOperator | MajoranaOperator

A MajoranaOperator or any object implementing get_majorana_operator().

paraminitial_stateSequence[int] | np.ndarray

Slater determinant, as occupied mode indices.

paramcutoffint

Bound on the complexity of the Majorana monomials retained during evolution, read according to cutoff_type. A fully paired monomial -- support made up entirely of complete pairs (m_\{2j-1\} m_\{2j\}) -- is kept regardless: only paired monomials contribute against a computational-basis state or Slater determinant.

paramschrodinger_cutoffint | None

= None

None (default) keeps the Heisenberg picture; an integer selects the Schrodinger picture and bounds the evolved state -- including its initialization from initial_state -- by the same notion as cutoff_type. Choose it at least as large as cutoff for comparable accuracy.

paramcutoff_typestr

= 'length'

"length" (default) bounds the number of Majorana operators in a monomial; "support" bounds the distinct orbitals it acts on. The fully-paired exception applies on top of either.

paramlower_atolNone | float

= None

Monomials with |coeff| \< lower_atol are discarded during evolution.

paramupper_atolNone | float

= None

Monomials with |coeff| > upper_atol are kept regardless of complexity.

paramcommMPI.Comm | None

= None

Optional MPI communicator (must outlive the simulator).

Returns

None

funcevolved_operator(self, parameters=None, *, atol=1e-12) -> MajoranaOperator

Return the evolved operator as a MajoranaOperator.

Equivalent to contract_partially with inplace=False, without touching the simulator state. Unlike the base method, which hands back the engine's raw index tuples, this wraps them into a Majorana operator carrying the propagator's mode count.

paramself

paramparametersParameterValues

= None

Variational parameter values (see expectation_value).

paramatolfloat

= 1e-12

Terms with |coeff| \< atol are dropped; 0.0 keeps all of them.

Returns

monoprop.majorana.MajoranaOperator

The evolved operator (Heisenberg picture) or evolved state (Schrodinger picture).

funcupdate_initial_operator(self, new_operator) -> None

Replace the initial operator (existing terms only).

Re-weights the initial operator the graph is evaluated against, without touching the evolution graph or rebuilding the simulator. Only the initial operator is affected -- the gates and their generator coefficients are unchanged.

paramself

paramnew_operatorMajoranaOperator

A MajoranaOperator whose terms replace the matching initial-operator coefficients.

Returns

None

funccutoff_type(self, new_cutoff_type) -> None

Set the cutoff type ("length" or "support").

paramself

paramnew_cutoff_typestr

Returns

None

funcbuild_graph(self, circuit, *, seed_parameters=None, only_rotate_len_k=None) -> None

Append a circuit to the propagation graph.

Builds (or extends) the reusable evolution graph, recording each layer's gate information (the parameter that drives it and its generator coefficient) so that later evaluation takes only parameters. The circuit's angle indices are local (0-based); when extending a non-empty graph they are shifted up onto the accumulated parameter axis automatically, so each call's circuit is authored independently.

paramself

paramcircuitCircuit

Gates to append, as a Circuit.

paramseed_parametersParameterValues

= None

The full parameter vector covering the whole accumulated graph, used to regenerate the coefficient seed (by contracting the existing graph) so coefficient truncation sees realistic coefficients when extending. Only needed when extending a non-empty graph with coefficient-informed truncation; on the first (or a single) call it defaults to the circuit's own parameters. When omitted while extending, the new layers are built structurally (coefficient truncation is skipped for them); the engine validates the length of an explicit seed.

paramonly_rotate_len_kint | None

= None

If provided, apply gates to Majorana monomials of length <= k in the evolved operator even if they anticommute. Useful when many free-fermionic gates (generators that are length-2 Majorana monomials) are applied before expectation-value estimation in Schrodinger-picture simulations.

Returns

None

funcsize(self) -> int

Number of Majorana monomials currently tracked.

paramself

Returns

int

The number of distinct Majorana monomial terms in the simulator's current representation.

MajoranaPropagator

Attributes

Functions

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