SimplifiedTwoDesignAnsatz#

class pyqit.ansatzes.SimplifiedTwoDesignAnsatz(n_qubits: int, n_layers: int = 2)[source]#

Bases: BaseAnsatz

Simplified two-design ansatz of Cerezo et al. 2021 (Nat. Commun.).

Wraps PennyLane’s SimplifiedTwoDesign: an initial RY layer, then n_layers of controlled-Z gates on alternating pairs each followed by RY rotations. This is the circuit the local-cost trainability result was proved on, so it pairs with the barren-plateau diagnostic.

There are two weight tensors, initial_layer_weights of shape (n_qubits,) and weights of shape (n_layers, n_qubits - 1, 2).

Parameters:

n_qubits (int) – At least 2.
n_layers (int, default 2)

References

Cerezo, Sone, Volkoff, Cincio, Coles, “Cost function dependent barren plateaus in shallow parametrized quantum circuits”, Nat. Commun. 12, 1791 (2021).

Examples

>>> from pyqit.ansatzes import SimplifiedTwoDesignAnsatz
>>> SimplifiedTwoDesignAnsatz(n_qubits=3, n_layers=2).get_weight_shapes()
{'initial_layer_weights': (3,), 'weights': (2, 2, 2)}

build_circuit(weights)[source]#: Apply the layers. Expects weights[“initial_layer_weights”] of shape (n_qubits,) and weights[“weights”] of shape (n_layers, n_qubits - 1, 2).

get_circuit_func()#: Returns the bound method to be passed to a QNode.

classmethod get_test_params()[source]#: List constructor kwargs used to parametrize this class in the test suite.

get_weight_shapes() → dict[source]#: Return the two weight shapes, initial_layer_weights and weights.