Functions:
qaoa_mixer_layer
qaoa_mixer_layer(
b: CReal,
target: QArray[QBit]
) -> None
[Qmod Classiq-library function]
Applies the mixer layer for the QAOA algorithm.
The mixer layer is a sequence of X gates applied to each qubit in the target quantum
array variable.
Parameters:
qaoa_cost_layer
qaoa_cost_layer(
g: CReal,
hamiltonian: CArray[PauliTerm],
target: QArray[QBit]
) -> None
[Qmod Classiq-library function]
Applies the cost layer to the QAOA model.
This function integrates the problem-specific cost function into the QAOA model’s objective function.
The cost layer represents the primary objective that the QAOA algorithm seeks to optimize, such as
minimizing energy or maximizing profit, depending on the application.
Parameters:
qaoa_layer
qaoa_layer(
g: CReal,
b: CReal,
hamiltonian: CArray[PauliTerm],
target: QArray[QBit]
) -> None
[Qmod Classiq-library function]
Applies the QAOA layer, which concatenates the cost layer and the mixer layer.
The qaoa_layer function integrates both the cost and mixer layers, essential components of the
Quantum Approximate Optimization Algorithm (QAOA). The cost layer encodes the problem’s objective,
while the mixer layer introduces quantum superposition and drives the search across the solution space.
Parameters:
qaoa_init
qaoa_init(
target: QArray[QBit]
) -> None
[Qmod Classiq-library function]
Initializes the QAOA circuit by applying the Hadamard gate to all qubits.
In the Quantum Approximate Optimization Algorithm (QAOA), the initial state is a uniform superposition
created by applying the Hadamard gate to each qubit. This function prepares the qubits for the subsequent
application of the cost and mixer layers by preparing them in an equal superposition state.
Parameters:
qaoa_penalty
qaoa_penalty(
num_qubits: CInt,
params_list: CArray[CReal],
hamiltonian: CArray[PauliTerm],
target: QArray[QBit, Literal[‘num_qubits’]]
) -> None
[Qmod Classiq-library function]
Applies the penalty layer to the QAOA model.
This function adds a penalty term to the objective function of the QAOA model to
enforce certain constraints (e.g., binary or integer variables) during the
optimization process.
Parameters:
