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, **kwargs) → 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=None)#

Bases: QibotnBackend, NumpyBackend

Creates CuQuantum backend for QiboTN.

configure_tn_simulation(runcard)#

Configure the TN simulation that will be performed.

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#

qibotn.backends.quimb.configure_tn_simulation(self, ansatz: str = 'mps', max_bond_dimension: Optional[int] = None, svd_cutoff: Optional[float] = 1e-10, n_most_frequent_states: int = 100)#

Configure tensor network simulation.

Parameters

ansatz – str, optional The tensor network ansatz to use. Default is None and, in this case, a generic Circuit Quimb class is used. 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.

qibotn.backends.quimb.execute_circuit(self, circuit: Circuit, initial_state=None, nshots=None, return_array=False)#

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.

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.

qibotn.backends.quimb.exp_value_observable_symbolic(self, circuit, operators_list, sites_list, coeffs_list, nqubits)#

Compute the expectation value of a symbolic Hamiltonian on a quantum circuit using tensor network contraction. This method takes a Qibo circuit, converts it to a Quimb tensor network circuit, and evaluates the expectation value of a Hamiltonian specified by three lists of strings: operators, sites, and coefficients. The expectation value is computed by summing the contributions from each term in the Hamiltonian, where each term’s expectation is calculated applying gates to the tensor network representation of the circuit and contracting it with the respective bra. Each operator string must act on all different qubits, i.e., for each term, the corresponding sites tuple must contain unique qubit indices. Example: operators_list = [‘xyz’, ‘xyz’], sites_list = [(1,2,3), (1,2,3)], coeffs_list = [1, 2]

Parameters

  • circuit (qibo.models.Circuit) – The quantum circuit to evaluate, provided as a Qibo circuit object.

  • operators_list (list of str) – List of operator strings representing the symbolic Hamiltonian terms.

  • sites_list (list of tuple of int) – Tuples each specifying the qubits (sites) the corresponding operator acts on.

  • coeffs_list (list of real/complex) – The coefficients for each Hamiltonian term.

Returns

The real part of the expectation value of the Hamiltonian on the given circuit state.

Return type

float

qibotn.backends.quimb.setup_backend_specifics(self, quimb_backend='numpy', contractions_optimizer='auto-hq')#

Setup backend specifics. :param quimb_backend: str

The backend to use for the quimb tensor network simulation.

Parameters

contractions_optimizer – str, optional The contractions_optimizer to use for the quimb tensor network simulation.