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Rename function name to be more descriptive [skip CI]

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This commit is contained in:
tankya2
2024-01-30 15:40:11 +08:00
committed by yangliwei
parent a0ad2af0c9
commit 93514a51f6
2 changed files with 11 additions and 9 deletions
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14
src/qibotn/eval.py
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@@ -10,19 +10,19 @@ from qibotn.QiboCircuitConvertor import QiboCircuitToEinsum
from qibotn.QiboCircuitToMPS import QiboCircuitToMPS
def eval(qibo_circ, datatype):
def dense_vector_tn(qibo_circ, datatype):
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(*myconvertor.state_vector_operands())
def eval_expectation(qibo_circ, datatype):
def expectation_tn(qibo_circ, datatype):
myconvertor = QiboCircuitToEinsum(qibo_circ, dtype=datatype)
return contract(
*myconvertor.expectation_operands(PauliStringGen(qibo_circ.nqubits))
)
def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
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.
@@ -118,7 +118,7 @@ def eval_tn_MPI(qibo_circ, datatype, n_samples=8):
return result, rank
def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
def dense_vector_tn_nccl(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI
import socket
from cuquantum import Network
@@ -204,7 +204,7 @@ def eval_tn_nccl(qibo_circ, datatype, n_samples=8):
return result, rank
def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
def expectation_tn_nccl(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI
import socket
from cuquantum import Network
@@ -291,7 +291,7 @@ def eval_tn_nccl_expectation(qibo_circ, datatype, n_samples=8):
return result, rank
def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
def expectation_tn_MPI(qibo_circ, datatype, n_samples=8):
from mpi4py import MPI # this line initializes MPI
import socket
from cuquantum import Network
@@ -370,7 +370,7 @@ def eval_tn_MPI_expectation(qibo_circ, datatype, n_samples=8):
return result, rank
def eval_mps(qibo_circ, gate_algo, datatype):
def dense_vector_mps(qibo_circ, gate_algo, datatype):
myconvertor = QiboCircuitToMPS(qibo_circ, gate_algo, dtype=datatype)
mps_helper = MPSContractionHelper(myconvertor.num_qubits)

6
tests/test_cuquantum_cutensor_backend.py
View File

@@ -39,7 +39,9 @@ def test_eval(nqubits: int, dtype="complex128"):
qibo_time, (qibo_circ, result_sv) = time(lambda: qibo_qft(nqubits, swaps=True))
# Test Cuquantum
cutn_time, result_tn = time(lambda: qibotn.cutn.eval(qibo_circ, dtype).flatten())
cutn_time, result_tn = time(
lambda: qibotn.eval.dense_vector_tn(qibo_circ, dtype).flatten()
)
assert 1e-2 * qibo_time < cutn_time < 1e2 * qibo_time
assert np.allclose(result_sv, result_tn), "Resulting dense vectors do not match"
@@ -74,7 +76,7 @@ def test_mps(nqubits: int, dtype="complex128"):
}
cutn_time, result_tn = time(
lambda: qibotn.eval.eval_mps(circ_qibo, gate_algo, dtype).flatten()
lambda: qibotn.eval.dense_vector_mps(circ_qibo, gate_algo, dtype).flatten()
)
print(f"State vector difference: {abs(result_tn - result_sv_cp).max():0.3e}")