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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot]
2024-02-08 09:18:18 +00:00
committed by yangliwei
parent b432be6d9c
commit 898ca877e9
10 changed files with 93 additions and 77 deletions
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89
src/qibotn/eval.py
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@@ -1,10 +1,6 @@
import multiprocessing
import cupy as cp
from cupy.cuda.runtime import getDeviceCount
from cuquantum import contract
from cupy.cuda.runtime import getDeviceCount
import cupy as cp
from qibotn.mps_contraction_helper import MPSContractionHelper
from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
@@ -12,13 +8,15 @@ from qibotn.QiboCircuitToMPS import QiboCircuitToMPS
def dense_vector_tn(qibo_circ, datatype):
"""Convert qibo circuit to tensornet (TN) format and perform contraction to dense vector."""
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
dense vector."""
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."""
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
expectation of given Pauli string."""
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(
*myconvertor.expectation_operands(
@@ -28,14 +26,19 @@ def expectation_pauli_tn(qibo_circ, datatype, pauli_string_pattern):
def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
"""Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through MPI.
The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
The pathfinder looks at user defined number of samples (n_samples) iteratively to 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 a dense vector representation of the TN.
"""Convert qibo circuit to tensornet (TN) format and perform contraction
using multi node and multi GPU through MPI.
The conversion is performed by QiboCircuitToEinsum(), after which it
goes through 2 steps: pathfinder and execution. The pathfinder looks
at user defined number of samples (n_samples) iteratively to 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 a dense vector representation of the TN.
"""
from mpi4py import MPI
from cuquantum import Network
from mpi4py import MPI
root = 0
comm = MPI.COMM_WORLD
@@ -90,14 +93,19 @@ def dense_vector_tn_MPI(qibo_circ, datatype, n_samples=8):
def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
"""Convert qibo circuit to tensornet (TN) format and perform contraction using multi node and multi GPU through NCCL.
The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
The pathfinder looks at user defined number of samples (n_samples) iteratively to 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 a dense vector representation of the TN.
"""Convert qibo circuit to tensornet (TN) format and perform contraction
using multi node and multi GPU through NCCL.
The conversion is performed by QiboCircuitToEinsum(), after which it
goes through 2 steps: pathfinder and execution. The pathfinder looks
at user defined number of samples (n_samples) iteratively to 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 a dense vector representation of the TN.
"""
from mpi4py import MPI
from cuquantum import Network
from cupy.cuda import nccl
from cuquantum import Network
from mpi4py import MPI
root = 0
comm_mpi = MPI.COMM_WORLD
@@ -163,15 +171,22 @@ def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_samples=8):
"""Convert qibo circuit to tensornet (TN) format and perform contraction to expectation of given Pauli string using multi node and multi GPU through NCCL.
The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
The pauli_string_pattern is used to generate the pauli string corresponding to the number of qubits of the system.
The pathfinder looks at user defined number of samples (n_samples) iteratively to 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.
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
expectation of given Pauli string using multi node and multi GPU through
NCCL.
The conversion is performed by QiboCircuitToEinsum(), after which it
goes through 2 steps: pathfinder and execution. The
pauli_string_pattern is used to generate the pauli string
corresponding to the number of qubits of the system. The pathfinder
looks at user defined number of samples (n_samples) iteratively to
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.
"""
from mpi4py import MPI
from cuquantum import Network
from cupy.cuda import nccl
from cuquantum import Network
from mpi4py import MPI
root = 0
comm_mpi = MPI.COMM_WORLD
@@ -239,14 +254,21 @@ def expectation_pauli_tn_nccl(qibo_circ, datatype, pauli_string_pattern, n_sampl
def expectation_pauli_tn_MPI(qibo_circ, datatype, pauli_string_pattern, n_samples=8):
"""Convert qibo circuit to tensornet (TN) format and perform contraction to expectation of given Pauli string using multi node and multi GPU through MPI.
The conversion is performed by QiboCircuitToEinsum(), after which it goes through 2 steps: pathfinder and execution.
The pauli_string_pattern is used to generate the pauli string corresponding to the number of qubits of the system.
The pathfinder looks at user defined number of samples (n_samples) iteratively to 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.
"""Convert qibo circuit to tensornet (TN) format and perform contraction to
expectation of given Pauli string using multi node and multi GPU through
MPI.
The conversion is performed by QiboCircuitToEinsum(), after which it
goes through 2 steps: pathfinder and execution. The
pauli_string_pattern is used to generate the pauli string
corresponding to the number of qubits of the system. The pathfinder
looks at user defined number of samples (n_samples) iteratively to
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.
"""
from mpi4py import MPI # this line initializes MPI
from cuquantum import Network
from mpi4py import MPI # this line initializes MPI
root = 0
comm = MPI.COMM_WORLD
@@ -303,7 +325,8 @@ 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."""
"""Convert qibo circuit to matrix product state (MPS) format and perform
contraction to dense vector."""
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
mps_helper = MPSContractionHelper(myconvertor.num_qubits)
@@ -313,7 +336,9 @@ def dense_vector_mps(qibo_circ, gate_algo, datatype):
def pauli_string_gen(nqubits, pauli_string_pattern):
"""Used internally to generate the string based on given pattern and number of qubit.
"""Used internally to generate the string based on given pattern and number
of qubit.
Example: pattern: "XZ", number of qubit: 7, output = XZXZXZX
"""
if nqubits <= 0: