mqt.problemsolver.satellite_solver.algorithms

Module Contents

solve_using_qaoa(qubo: numpy.typing.NDArray[numpy.float64], noisy_flag: bool = True, layers: int = 10, num_init: int = 5) → float

solve_using_vqe(qubo: numpy.typing.NDArray[numpy.float64], noisy_flag: bool = True, ansatz: QuantumCircuit | None = None, num_init: int = 5) → float

bitstring_to_vector(bitstring: str) → numpy.typing.NDArray[int_]

Convert a bitstring to a vector of integers.

compute_expectation(counts: dict[str, int], qubo: numpy.typing.NDArray[numpy.float64]) → float

Computes the expectation value of the quadratic form Q with respect to the given counts.

Parameters:

• counts – A dictionary where keys are bitstrings and values are their counts.

• qubo – The QUBO matrix represented as a NumPy array.

Returns:

float

The expectation value of the quadratic form Q with respect to the counts.

cost_func(params: numpy.typing.NDArray[numpy.float64], circuit: QuantumCircuit, backend: BackendV2, qubo: numpy.typing.NDArray[numpy.float64]) → float

Computes the cost function for QAOA optimization.

Parameters:

• params – An array of floats representing the parameters for the QAOA circuit

• circuit – The QuantumCircuit representing the QAOA circuit to be evaluated.

• backend – The backend to run the circuit on.

• qubo – The QUBO matrix representing the problem.

Returns:

A float representing the expectation value of the QUBO Hamiltonian with respect to the states produced by the circuit.

optimize_with_multiple_init_parameters(num_init: int, circuit: QuantumCircuit, backend: BackendV2, qubo: numpy.typing.NDArray[numpy.float64]) → tuple[list[float], QuantumCircuit]

Optimizes the QAOA parameters using multiple random initializations.

Parameters:

• num_init – The number of random initializations to perform.

• circuit – The QuantumCircuit representing the QAOA circuit to be optimized.

• backend – The backend to run the circuit on.

• qubo – The QUBO matrix representing the problem.

Returns:

A list of floats representing the optimized parameters that minimize the cost function.

evaluate_result(num_init: int, circuit: QuantumCircuit, backend: BackendV2, qubo: numpy.typing.NDArray[numpy.float64]) → tuple[str, int, float]

Evaluates the result of the QAOA circuit.

Parameters:

• num_init – The number of initializations to perform.

• circuit – The QAOA circuit to be evaluated.

• backend – The backend to run the circuit on.

• qubo – The QUBO matrix representing the problem.

Returns:

• The found state as a bitstring.

• The count of that state.

• The energy computed from the QUBO matrix.

class QAOA(qubo: numpy.typing.NDArray[numpy.float64], layers: int, num_init: int, backend: BackendV2, reps: int = 2)

qubo

layers

num_init

backend

reps = 2

get_solution() → tuple[QuantumCircuit, float]

Returns the quantum circuit of the QAOA algorithm and the resulting solution.

Returns:

• The QAOA circuit (QuantumCircuit) of the solution.

• The computed energy (float) of the solution.

class VQE(qubo: numpy.typing.NDArray[numpy.float64], backend: BackendV2, ansatz: QuantumCircuit | None = None, num_init: int = 1)

qubo

backend

num_init = 1

get_solution() → tuple[QuantumCircuit, float]

Returns the quantum circuit of the VQE algorithm and the resulting solution.

Returns:

• The VQE circuit (QuantumCircuit) of the solution.

• The computed energy (float) of the solution.