qibotn.backends package#

class qibotn.backends.MetaBackend#

Bases: object

Meta-backend class which takes care of loading the qibotn backends.

list_available() → dict#

Lists all the available qibotn backends.

static load(platform: str, runcard: dict = None) → QibotnBackend#

Loads the backend.

Parameters:

  • platform (str) – Name of the backend to load: either cutensornet or qutensornet.

  • runcard (dict) – Dictionary containing the simulation settings.

Returns:

The loaded backend.

Return type:

qibo.backends.abstract.Backend

Submodules#

qibotn.backends.abstract module#

class qibotn.backends.abstract.QibotnBackend#

Bases: ABC

apply_gate(gate, state, nqubits)#
apply_gate_density_matrix(gate, state, nqubits)#
assign_measurements(measurement_map, circuit_result)#
configure_tn_simulation(**config)#

Configure the TN simulation that will be performed.

set_device(device)#
set_precision(precision)#

qibotn.backends.cutensornet module#

class qibotn.backends.cutensornet.CuTensorNet(runcard)#

Bases: QibotnBackend, NumpyBackend

Creates CuQuantum backend for QiboTN.

cuda_type(dtype='complex64')#

Get CUDA Type.

Parameters:

dtype (str, optional) – Either single (“complex64”) or double (complex128) precision. Defaults to “complex64”.

Returns:

tuple of cuquantum.cudaDataType and cuquantum.ComputeType

Return type:

CUDA Type

execute_circuit(circuit, initial_state=None, nshots=None, return_array=False)#

Executes a quantum circuit using selected TN backend.

Parameters:

  • circuit (qibo.models.circuit.Circuit) – Circuit to execute.

  • initial_state (qibo.models.circuit.Circuit) – Circuit to prepare the initial state. If None the default |00...0> state is used.

Returns:

If return_array is False, returns a QuantumState object representing the quantum state. If return_array is True, returns a numpy array representing the quantum state.

Return type:

QuantumState or numpy.ndarray

qibotn.backends.qmatchatea module#

qibotn.backends.quimb module#

class qibotn.backends.quimb.QuimbBackend#

Bases: QibotnBackend, NumpyBackend

configure_tn_simulation(ansatz: str = 'MPS', max_bond_dimension: int = 10, n_most_frequent_states: int = 100)#

Configure tensor network simulation.

Parameters:

  • ansatz – str, optional The tensor network ansatz to use. Currently, only “MPS” is supported. Default is “MPS”.

  • max_bond_dimension – int, optional The maximum bond dimension for the MPS ansatz. Default is 10.

Notes

  • The ansatz determines the tensor network structure used for simulation. Currently, only “MPS” is supported.

  • The max_bond_dimension parameter controls the maximum allowed bond dimension for the MPS ansatz.

execute_circuit(circuit, initial_state=None, nshots=None, return_array=False, **prob_kwargs)#

Execute a quantum circuit using the specified tensor network ansatz and initial state.

Parameters:

  • circuit – QuantumCircuit The quantum circuit to be executed.

  • initial_state – array-like, optional The initial state of the quantum system. Only supported for Matrix Product States (MPS) ansatz.

  • nshots – int, optional The number of shots for sampling the circuit. If None, no sampling is performed, and the full statevector is used.

  • return_array – bool, optional If True, returns the statevector as a dense array. Default is False.

  • n_most_frequent_states – int, optional The number of most frequent computational basis states to return. Default is 100.

  • **prob_kwargs – dict, optional Additional keyword arguments for probability computation (currently unused).

Returns:

TensorNetworkResult

An object containing the results of the circuit execution, including: - nqubits: Number of qubits in the circuit. - backend: The backend used for execution. - measures: The measurement frequencies if nshots is specified, otherwise None. - measured_probabilities: A dictionary of computational basis states and their probabilities. - prob_type: The type of probability computation used (currently “default”). - statevector: The final statevector as a dense array if return_array is True, otherwise None.

Raises:

ValueError – If an initial state is provided but the ansatz is not “MPS”.

Notes

  • The ansatz determines the tensor network structure used for simulation. Currently, only “MPS” is supported.

  • If initial_state is provided, it must be compatible with the MPS ansatz.

  • The nshots parameter enables sampling from the circuit’s output distribution. If not specified, the full statevector is computed.

setup_backend_specifics(qimb_backend='numpy')#

Setup backend specifics. :param qimb_backend: str

The backend to use for the quimb tensor network simulation.