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Update docstrings and refine

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This commit is contained in:
tankya2
2024-03-04 15:49:06 +08:00
parent f295bfd276
commit ac9fa2106e
4 changed files with 89 additions and 16 deletions
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9
src/qibotn/backends/cutensornet.py
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@@ -95,12 +95,9 @@ class CuTensorNet(NumpyBackend): # pragma: no cover
def cuda_type(self, dtype="complex64"):
"""Get CUDA Type.
Args:
Parameters:
dtype (str, optional): Either single ("complex64") or double (complex128) precision. Defaults to "complex64".
Raises:
TypeError: dtype either complex64 or complex128
Returns:
CUDA Type: tuple of cuquantum.cudaDataType and cuquantum.ComputeType
"""
@@ -114,13 +111,13 @@ class CuTensorNet(NumpyBackend): # pragma: no cover
): # pragma: no cover
"""Executes a quantum circuit using selected TN backend.
Args:
Parameters:
circuit (:class:`qibo.models.circuit.Circuit`): Circuit to execute.
initial_state (:class:`qibo.models.circuit.Circuit`): Circuit to prepare the initial state.
If ``None`` the default ``|00...0>`` state is used.
Returns:
QuantumState if return_array=False. Numpy array if return_array=True.
QuantumState or numpy.ndarray: 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.
"""
import qibotn.eval as eval

19
src/qibotn/circuit_convertor.py
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@@ -26,9 +26,12 @@ class QiboCircuitToEinsum:
self.circuit = circuit
def state_vector_operands(self):
"""Create the operands for expectation computation in the interleave
"""Create the operands for dense vector computation in the interleave
format.
Parameters:
None
Returns:
Operands for the contraction in the interleave format.
"""
@@ -85,8 +88,14 @@ class QiboCircuitToEinsum:
return mode_labels, operands
def op_shape_from_qubits(self, nqubits):
"""Modify tensor to cuQuantum shape (qubit_states,input_output) *
qubits_involved."""
"""Modify tensor to cuQuantum shape
Parameters:
nqubits (int): The number of qubits in quantum circuit.
Returns:
(qubit_states,input_output) * nqubits
"""
return (2, 2) * nqubits
def init_intermediate_circuit(self, circuit):
@@ -175,7 +184,7 @@ class QiboCircuitToEinsum:
def get_pauli_gates(self, pauli_map, dtype="complex128", backend=cp):
"""Populate the gates for all pauli operators.
Args:
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.
@@ -202,7 +211,7 @@ class QiboCircuitToEinsum:
"""Create the operands for pauli string expectation computation in the
interleave format.
Args:
Parameters:
pauli_string: A string representating the list of pauli gates.
Returns:

73
src/qibotn/eval.py
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@@ -9,14 +9,31 @@ from qibotn.mps_contraction_helper import MPSContractionHelper
def dense_vector_tn(qibo_circ, datatype):
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
dense vector."""
dense vector.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
Returns:
Dense vector of quantum circuit.
"""
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(*myconvertor.state_vector_operands())
def expectation_pauli_tn(qibo_circ, datatype, pauli_string_pattern):
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
expectation of given Pauli string."""
expectation of given Pauli string.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
pauli_string_pattern(str): pauli string pattern.
Returns:
Expectation of quantum circuit due to pauli string.
"""
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(
*myconvertor.expectation_operands(
@@ -35,6 +52,14 @@ def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
the least costly contraction path. This is sped up with multi
thread. After pathfinding the optimal path is used in the actual
contraction to give a dense vector representation of the TN.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
n_samples(int): Number of samples for pathfinding.
Returns:
Dense vector of quantum circuit.
"""
from cuquantum import Network
@@ -102,6 +127,14 @@ def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
the least costly contraction path. This is sped up with multi
thread. After pathfinding the optimal path is used in the actual
contraction to give a dense vector representation of the TN.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
n_samples(int): Number of samples for pathfinding.
Returns:
Dense vector of quantum circuit.
"""
from cupy.cuda import nccl
from cuquantum import Network
@@ -183,6 +216,15 @@ def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_sampl
select the least costly contraction path. This is sped up with multi
thread. After pathfinding the optimal path is used in the actual
contraction to give an expectation value.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
pauli_string_pattern(str): pauli string pattern.
n_samples(int): Number of samples for pathfinding.
Returns:
Expectation of quantum circuit due to pauli string.
"""
from cupy.cuda import nccl
from cuquantum import Network
@@ -266,6 +308,15 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_sample
select the least costly contraction path. This is sped up with multi
thread. After pathfinding the optimal path is used in the actual
contraction to give an expectation value.
Parameters:
qibo_circ: The quantum circuit object.
datatype (str): Either single ("complex64") or double (complex128) precision.
pauli_string_pattern(str): pauli string pattern.
n_samples(int): Number of samples for pathfinding.
Returns:
Expectation of quantum circuit due to pauli string.
"""
from cuquantum import Network
from mpi4py import MPI # this line initializes MPI
@@ -326,7 +377,16 @@ def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_sample
def dense_vector_mps(qibo_circ, gate_algo, datatype):
"""Convert qibo circuit to matrix product state (MPS) format and perform
contraction to dense vector."""
contraction to dense vector.
Parameters:
qibo_circ: The quantum circuit object.
gate_algo(dict): Dictionary for SVD and QR settings.
datatype (str): Either single ("complex64") or double (complex128) precision.
Returns:
Dense vector of quantum circuit.
"""
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
mps_helper = MPSContractionHelper(myconvertor.num_qubits)
@@ -339,6 +399,13 @@ def pauli_string_gen(nqubits, pauli_string_pattern):
"""Used internally to generate the string based on given pattern and number
of qubit.
Parameters:
nqubits(int): Number of qubits of Quantum Circuit
pauli_string_pattern(str): Strings representing sequence of pauli gates.
Returns:
String representation of the actual pauli string from the pattern.
Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX
"""
if nqubits <= 0:

2
src/qibotn/mps_contraction_helper.py
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@@ -10,7 +10,7 @@ class MPSContractionHelper:
Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb
The follwing compute quantities are supported:
The following compute quantities are supported:
- the norm of the MPS.
- the equivalent state vector from the MPS.