qibotn package#

Subpackages#

Submodules#

qibotn.circuit_convertor module#

class qibotn.circuit_convertor.QiboCircuitToEinsum(circuit, dtype='complex128')#

Bases: object

Convert a circuit to a Tensor Network (TN) representation.

The circuit is first processed to an intermediate form by grouping each gate matrix with its corresponding qubit it is acting on to a list. It is then converted to an equivalent TN expression through the class function state_vector_operands() following the Einstein summation convention in the interleave format.

See document for detail of the format: https://docs.nvidia.com/cuda/cuquantum/python/api/generated/cuquantum.contract.html

The output is to be used by cuQuantum’s contract() for computation of the state vectors of the circuit.

expectation_operands(ham_gates)#

Create the operands for pauli string expectation computation in the interleave format.

Parameters:

ham_gates – A list of gates derived from Qibo hamiltonian object.

Returns:

Operands for the contraction in the interleave format.

get_pauli_gates(pauli_map, dtype='complex128', backend=cupy)#

Populate the gates for all pauli operators.

Parameters:

  • pauli_map – A dictionary mapping qubits to pauli operators.

  • dtype – Data type for the tensor operands.

  • backend – The package the tensor operands belong to.

Returns:

A sequence of pauli gates.

init_basis_map(backend, dtype)#

Initialize the basis map for the quantum circuit.

This method initializes a basis map for the quantum circuit, which maps binary strings representing qubit states to their corresponding quantum state vectors.

Parameters:

  • backend (object) – The backend object providing the array conversion method.

  • dtype (object) – The data type for the quantum state vectors.

init_intermediate_circuit(circuit)#

Initialize the intermediate circuit representation.

This method initializes the intermediate circuit representation by extracting gate matrices and qubit IDs from the given quantum circuit.

Parameters:

circuit (object) – The quantum circuit object.

init_inverse_circuit(circuit)#

Initialize the inverse circuit representation.

This method initializes the inverse circuit representation by extracting gate matrices and qubit IDs from the given quantum circuit.

Parameters:

circuit (object) – The quantum circuit object.

op_shape_from_qubits(nqubits)#

Modify tensor to cuQuantum shape.

Parameters:

nqubits (int) – The number of qubits in quantum circuit.

Returns:

(qubit_states,input_output) * nqubits

state_vector_operands()#

Create the operands for dense vector computation in the interleave format.

Returns:

Operands for the contraction in the interleave format.

qibotn.circuit_to_mps module#

class qibotn.circuit_to_mps.QiboCircuitToMPS(circ_qibo, gate_algo, dtype='complex128', rand_seed=0)#

Bases: object

A helper class to convert Qibo circuit to MPS.

Parameters:

  • circ_qibo – The quantum circuit object.

  • gate_algo (dict) – Dictionary for SVD and QR settings.

  • datatype (str) – Either single (“complex64”) or double (complex128) precision.

  • rand_seed (int) – Seed for random number generator.

qibotn.eval module#

qibotn.eval_qu module#

qibotn.eval_qu.dense_vector_tn_qu(qasm: str, initial_state, mps_opts, backend='numpy')#

Evaluate circuit in QASM format with Quimb.

Parameters:

  • qasm (str) – QASM program.

  • initial_state (list) – Initial state in the dense vector form. If None the default |00...0> state is used.

  • mps_opts (dict) – Parameters to tune the gate_opts for mps settings in class quimb.tensor.circuit.CircuitMPS.

  • backend (str) – Backend to perform the contraction with, e.g. numpy, cupy, jax. Passed to opt_einsum.

Returns:

Amplitudes of final state after the simulation of the circuit.

Return type:

list

qibotn.eval_qu.init_state_tn(nqubits, init_state_sv)#

Create a matrix product state directly from a dense vector.

Parameters:

  • nqubits (int) – Total number of qubits in the circuit.

  • init_state_sv (list) – Initial state in the dense vector form.

Returns:

Matrix product state representation of the dense vector.

Return type:

list

qibotn.mps_contraction_helper module#

class qibotn.mps_contraction_helper.MPSContractionHelper(num_qubits)#

Bases: object

A helper class to compute various quantities for a given MPS.

Interleaved format is used to construct the input args for cuquantum.contract.

Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb

The following compute quantities are supported:

  • the norm of the MPS.

  • the equivalent state vector from the MPS.

  • the expectation value for a given operator.

  • the equivalent state vector after multiplying an MPO to an MPS.

Parameters:

num_qubits – The number of qubits for the MPS.

contract_expectation(mps_tensors, operator, qubits, options=None, normalize=False)#

Contract the corresponding tensor network to form the expectation of the MPS.

Parameters:

  • mps_tensors – A list of rank-3 ndarray-like tensor objects. The indices of the ith tensor are expected to be bonding index to the i-1 tensor, the physical mode, and then the bonding index to the i+1th tensor.

  • operator – A ndarray-like tensor object. The modes of the operator are expected to be output qubits followed by input qubits, e.g, A, B, a, b where a, b denotes the inputs and A, B’ denotes the outputs.

  • qubits – A sequence of integers specifying the qubits that the operator is acting on.

  • options – Specify the contract and decompose options.

  • normalize – Whether to scale the expectation value by the normalization factor.

Returns:

An ndarray-like object as the state vector.

contract_norm(mps_tensors, options=None)#

Contract the corresponding tensor network to form the norm of the MPS.

Parameters:

  • mps_tensors – A list of rank-3 ndarray-like tensor objects. The indices of the ith tensor are expected to be bonding index to the i-1 tensor, the physical mode, and then the bonding index to the i+1th tensor.

  • options – Specify the contract and decompose options.

Returns:

The norm of the MPS.

contract_state_vector(mps_tensors, options=None)#

Contract the corresponding tensor network to form the state vector representation of the MPS.

Parameters:

  • mps_tensors – A list of rank-3 ndarray-like tensor objects. The indices of the ith tensor are expected to be bonding index to the i-1 tensor, the physical mode, and then the bonding index to the i+1th tensor.

  • options – Specify the contract and decompose options.

Returns:

An ndarray-like object as the state vector.

qibotn.mps_utils module#

qibotn.mps_utils.apply_gate(mps_tensors, gate, qubits, **kwargs)#

Apply the gate operand to the MPS tensors in-place.

# Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb

Parameters:

  • mps_tensors – A list of rank-3 ndarray-like tensor objects. The indices of the ith tensor are expected to be the bonding index to the i-1 tensor, the physical mode, and then the bonding index to the i+1th tensor.

  • gate – A ndarray-like tensor object representing the gate operand. The modes of the gate is expected to be output qubits followed by input qubits, e.g, A, B, a, b where a, b denotes the inputs and A, B denotes the outputs.

  • qubits – A sequence of integers denoting the qubits that the gate is applied onto.

  • algorithm – The contract and decompose algorithm to use for gate application. Can be either a dict or a ContractDecomposeAlgorithm.

  • options – Specify the contract and decompose options.

Returns:

The updated MPS tensors.

qibotn.mps_utils.initial(num_qubits, dtype)#

Generate the MPS with an initial state of \(\ket{00...00}\)

Parameters:

  • num_qubits – Number of qubits in the Quantum Circuit.

  • dtype – Either single (“complex64”) or double (complex128) precision.

Returns:

The initial MPS tensors.

qibotn.mps_utils.mps_site_right_swap(mps_tensors, i, **kwargs)#

Perform the swap operation between the ith and i+1th MPS tensors.

Parameters:

  • mps_tensors – Tensors representing MPS

  • i (int) – index of the tensor to swap

Returns:

The updated MPS tensors.

qibotn.result module#

class qibotn.result.TensorNetworkResult(nqubits: int, backend: QibotnBackend, measures: dict, measured_probabilities: Union[dict, ndarray], prob_type: str, statevector: ndarray)#

Bases: object

Object to store and process the output of a Tensor Network simulation of a quantum circuit.

Parameters:

  • nqubits (int) – number of qubits involved in the simulation;

  • backend (QibotnBackend) – specific backend on which the simulation has been performed;

  • measures (dict) – measures (if performed) during the tensor network simulation;

  • measured_probabilities (Union[dict, ndarray]) – probabilities of the final state according to the simulation;

  • prob_type (str) – string identifying the method used to compute the probabilities. Especially useful in case the QmatchateaBackend is selected.

  • statevector (ndarray) – if computed, the reconstructed statevector.

backend: QibotnBackend#
frequencies()#

Return frequencies if a certain number of shots has been set.

measured_probabilities: Union[dict, ndarray]#
measures: dict#
nqubits: int#
prob_type: str#
probabilities()#

Return calculated probabilities according to the given method.

state()#

Return the statevector if the number of qubits is less than 20.

statevector: ndarray#