MonomialPropagator

Size of the current graph's parameter axis.

Follows the graph rather than accumulating: build_graph extends it, a re-wire through parameter_mapping resets it, and an in-place contract_partially shrinks it to the axis of the layers that are left.

Number of distinct authoring gates currently in the graph.

A multi-term gate expands to several layers sharing one gate, so n_gates \<= graph_layers.

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

Entry i is the parameter index driving graph layer i (one generated monomial), in parameter-vector order -- finer-grained than the authoring circuit's per-gate mapping.

Number of modes the simulator acts on (qubits, in the Pauli basis).

Number of evolved monomials (graph layers).

Whether the simulator is in the Schrodinger picture (else Heisenberg).

Current cutoff value for the simulation.

Current lower absolute tolerance for the cutoff function (None if unset).

Current upper absolute tolerance for the cutoff function (None if unset).

Current cutoff type, "length" or "support"; read-only outside the Majorana front-end.

Construct a propagator from a circuit and propagate it in one step.

The circuit supplies both the gates and the initial state; **config is forwarded to the constructor (cutoff, etc.).

Append a circuit to the propagation graph.

Builds (or extends) the reusable evolution graph, recording each layer's driving parameter and generator coefficient so later evaluation takes only parameters. A circuit's angle indices are local (0-based) and shift onto the accumulated axis when extending.

Evolve and contract immediately, without storing a graph.

Retains no graph, so it is cheaper than build_graph but is one-shot, at the circuit's own parameters.

Compute the expectation value at parameters.

Replays the stored graph against the current initial operator and reference state.

Compute the expectation value and gradient in a single backward pass over the graph.

Compute the gradient at parameters, as float64 in parameter-axis order.

Return a reusable callable computing the expectation value from parameters.

Return a reusable callable computing (expectation value, gradient).

Like expectation_value_functional, but one backward pass also yields the gradient.

Shorthand for expectation_value.

See expectation_value for full documentation.

Shorthand for gradient.

See gradient for full documentation.

Shorthand for expectation_value_and_gradient.

See expectation_value_and_gradient for full documentation.

Shorthand for expectation_value_functional.

See expectation_value_functional for full documentation.

Shorthand for expectation_value_and_gradient_functional.

See expectation_value_and_gradient_functional for full documentation.

Contract the graph into the operator/state at parameters.

Folds the stored gates, evaluated at parameters, into the operand -- the initial operator in the Heisenberg picture, the reference state in the Schrodinger picture.

Return the evolved operator/state without modifying simulator state.

Equivalent to contract_partially with inplace=False, decoded into a term dict.

Replace coefficients of the initial operator (existing terms only).

A re-weight, not a rebuild: the graph, its gates, and their generator coefficients are kept.

Number of distinct monomial terms in the simulator's current representation.

Size metrics of the evolution graph.