Fixed Test cases

setup.py

@@ -2,7 +2,9 @@
 from setuptools import setup, find_packages
 from setuptools.command.test import test as TestCommand
 
-sys.path.append("tnbs/BTC_01_PL")
+#sys.path.append("tnbs/BTC_01_PL")
+#sys.path.append("tnbs/BTC_02_AE")
+#sys.path.append("tnbs/BTC_03_QPE")
 
 class PyTest(TestCommand):
     """

tests/test_btc_01_pl.py

@@ -3,8 +3,12 @@
 import sys
 import pandas as pd
 import numpy as np
+import re
 
-sys.path.append("../tnbs/BTC_01_PL")
+folder = os.getcwd()
+folder = re.sub(r"WP3_Benchmark/.*", "WP3_Benchmark/", folder)
+folder = folder + "tnbs/BTC_01_PL"
+sys.path.append(folder)
 from my_benchmark_execution import KERNEL_BENCHMARK 
 
 
@@ -75,4 +79,4 @@ def test_pl():
     assert(100* list(a['KS']['mean'])[0] < 1.0)
 
     shutil.rmtree(folder)
-test_pl()
+#test_pl()

tests/test_btc_02_ae.py

@@ -0,0 +1,189 @@
+import os
+import shutil
+import sys
+import pandas as pd
+import numpy as np
+import re
+
+folder = os.getcwd()
+folder = re.sub(r"WP3_Benchmark/.*", "WP3_Benchmark/", folder)
+folder = folder + "tnbs/BTC_02_AE"
+sys.path.append(folder)
+from my_benchmark_execution import KERNEL_BENCHMARK 
+
+
+def create_folder(folder_name):
+    """
+    Check if folder exist. If not the function creates it
+
+    Parameters
+    ----------
+
+    folder_name : str
+        Name of the folder
+
+    Returns
+    ----------
+
+    folder_name : str
+        Name of the folder
+    """
+
+    # Check if the folder already exists
+    if not os.path.exists(folder_name):
+        # If it does not exist, create the folder
+        os.mkdir(folder_name)
+        print(f"Folder '{folder_name}' created.")
+    else:
+        print(f"Folder '{folder_name}' already exists.")
+    if folder_name.endswith('/') != True:
+        folder_name = folder_name + "/"
+    return folder_name
+
+def test_ae_iqae(): 
+
+    kernel_configuration ={
+        "ae_type": "IQAE",
+        "epsilon": 0.001,
+        "alpha": 0.05, # This the alpha for the AE algorithm
+        "multiplexor":  True,
+        "shots": 1000,
+        "qpu": "c",
+        "integrals": [0]
+    }
+    name = "AE_{}".format(kernel_configuration["ae_type"])
+
+    benchmark_arguments = {
+        #Pre benchmark configuration
+        "pre_benchmark": True,
+        "pre_samples": None,
+        "pre_save": True,
+        #Saving configuration
+        "save_append" : True,
+        "saving_folder": "./Results/",
+        "benchmark_times": "{}_times_benchmark.csv".format(name),
+        "csv_results": "{}_benchmark.csv".format(name),
+        "summary_results": "{}_SummaryResults.csv".format(name),
+        #Computing Repetitions configuration
+        "alpha": None,
+        "min_meas": None,
+        "max_meas": 10,
+        "relative_error": None,
+        "absolute_error": None,
+        #List number of qubits tested
+        "list_of_qbits": [4],
+    }
+
+    benchmark_arguments.update({"kernel_configuration": kernel_configuration})
+    folder = create_folder(benchmark_arguments["saving_folder"])
+    ae_bench = KERNEL_BENCHMARK(**benchmark_arguments)
+    ae_bench.exe()
+    filename = folder + benchmark_arguments["summary_results"]
+    a = pd.read_csv(filename, header=[0, 1], index_col=[0, 1])
+    #print(a["absolute_error_sum"]["mean"])
+    #print(100* list(a['KS']['mean'])[0])
+    assert(100* list(a['absolute_error_sum']['mean'])[0] < 1.0)
+    shutil.rmtree(folder)
+
+def test_ae_mlae(): 
+
+    kernel_configuration ={
+        "ae_type": "MLAE",
+        "schedule": [
+                [1, 2, 4, 8, 12],
+                [100, 100, 100, 100, 100]
+         ],
+        "delta": 1.0e-7,
+        "ns": 10000,
+        "mcz_qlm": False,
+        "multiplexor":  True,
+        "shots": 1000,
+        "qpu": "c",
+        "integrals": [0]
+    }
+    name = "AE_{}".format(kernel_configuration["ae_type"])
+
+    benchmark_arguments = {
+        #Pre benchmark configuration
+        "pre_benchmark": True,
+        "pre_samples": None,
+        "pre_save": True,
+        #Saving configuration
+        "save_append" : True,
+        "saving_folder": "./Results/",
+        "benchmark_times": "{}_times_benchmark.csv".format(name),
+        "csv_results": "{}_benchmark.csv".format(name),
+        "summary_results": "{}_SummaryResults.csv".format(name),
+        #Computing Repetitions configuration
+        "alpha": None,
+        "min_meas": None,
+        "max_meas": 10,
+        "relative_error": None,
+        "absolute_error": None,
+        #List number of qubits tested
+        "list_of_qbits": [4],
+    }
+
+    benchmark_arguments.update({"kernel_configuration": kernel_configuration})
+    folder = create_folder(benchmark_arguments["saving_folder"])
+    ae_bench = KERNEL_BENCHMARK(**benchmark_arguments)
+    ae_bench.exe()
+    filename = folder + benchmark_arguments["summary_results"]
+    a = pd.read_csv(filename, header=[0, 1], index_col=[0, 1])
+    #print(a["absolute_error_sum"]["mean"])
+    #print(100* list(a['KS']['mean'])[0])
+    assert(100* list(a['absolute_error_sum']['mean'])[0] < 1.0)
+    shutil.rmtree(folder)
+
+def test_ae_rqae(): 
+
+    kernel_configuration ={
+        "ae_type": "RQAE",
+        "epsilon": 0.01,
+        "gamma": 0.05,
+        "q": 1.2,
+
+        "mcz_qlm": False,
+        "multiplexor":  True,
+        "shots": 1000,
+        "qpu": "c",
+        "integrals": [0, 1]
+    }
+    name = "AE_{}".format(kernel_configuration["ae_type"])
+
+    benchmark_arguments = {
+        #Pre benchmark configuration
+        "pre_benchmark": True,
+        "pre_samples": None,
+        "pre_save": True,
+        #Saving configuration
+        "save_append" : True,
+        "saving_folder": "./Results/",
+        "benchmark_times": "{}_times_benchmark.csv".format(name),
+        "csv_results": "{}_benchmark.csv".format(name),
+        "summary_results": "{}_SummaryResults.csv".format(name),
+        #Computing Repetitions configuration
+        "alpha": None,
+        "min_meas": None,
+        "max_meas": 10,
+        "relative_error": None,
+        "absolute_error": None,
+        #List number of qubits tested
+        "list_of_qbits": [4],
+    }
+
+    benchmark_arguments.update({"kernel_configuration": kernel_configuration})
+    folder = create_folder(benchmark_arguments["saving_folder"])
+    ae_bench = KERNEL_BENCHMARK(**benchmark_arguments)
+    ae_bench.exe()
+    filename = folder + benchmark_arguments["summary_results"]
+    a = pd.read_csv(filename, header=[0, 1], index_col=[0, 1])
+    print(a["absolute_error_sum"]["mean"])
+    assert((a["absolute_error_sum"]["mean"] < 0.01).all())
+    #print(100* list(a['KS']['mean'])[0])
+    #assert(100* list(a['absolute_error_sum']['mean'])[0] < 1.0)
+    shutil.rmtree(folder)
+
+#test_ae_iqae()
+#test_ae_mlae()
+#test_ae_rqae()

tests/test_btc_03_qpe.py

@@ -0,0 +1,85 @@
+import os
+import shutil
+import sys
+import pandas as pd
+import numpy as np
+import re
+
+folder = os.getcwd()
+folder = re.sub(r"WP3_Benchmark/.*", "WP3_Benchmark/", folder)
+folder = folder + "tnbs/BTC_03_QPE"
+sys.path.append(folder)
+from my_benchmark_execution import KERNEL_BENCHMARK 
+
+
+def create_folder(folder_name):
+    """
+    Check if folder exist. If not the function creates it
+
+    Parameters
+    ----------
+
+    folder_name : str
+        Name of the folder
+
+    Returns
+    ----------
+
+    folder_name : str
+        Name of the folder
+    """
+
+    # Check if the folder already exists
+    if not os.path.exists(folder_name):
+        # If it does not exist, create the folder
+        os.mkdir(folder_name)
+        print(f"Folder '{folder_name}' created.")
+    else:
+        print(f"Folder '{folder_name}' already exists.")
+    if folder_name.endswith('/') != True:
+        folder_name = folder_name + "/"
+    return folder_name
+
+def test_qpe(): 
+
+    kernel_configuration = {
+        "angles" : ["random", 'exact'],
+        "auxiliar_qbits_number" : [8],
+        "qpu" : "c", #qlmass, python, c
+        "fidelity_error" : None,
+        "ks_error" : None,
+        "time_error": None
+    }
+
+    benchmark_arguments = {
+        #Pre benchmark sttuff
+        "pre_benchmark": True,
+        "pre_samples": None,
+        "pre_save": True,
+        #Saving stuff
+        "save_append" : True,
+        "saving_folder": "./Results/",
+        "benchmark_times": "kernel_times_benchmark.csv",
+        "csv_results": "kernel_benchmark.csv",
+        "summary_results": "kernel_SummaryResults.csv",
+        #Computing Repetitions stuff
+        "alpha": None,
+        "min_meas": None,
+        "max_meas": None,
+        #List number of qubits tested
+        "list_of_qbits": [6],
+    }
+
+    benchmark_arguments.update({"kernel_configuration": kernel_configuration})
+    folder = create_folder(benchmark_arguments["saving_folder"])
+    kernel_bench = KERNEL_BENCHMARK(**benchmark_arguments)
+    kernel_bench.exe()
+    filename = "./Results/kernel_SummaryResults.csv"
+    index_columns = [0, 1, 2, 3, 4, 5]
+    a = pd.read_csv(filename, header=[0, 1], index_col=index_columns)
+    a= a.reset_index()
+    assert((a[a['angle_method']=="exact"]['fidelity']['mean'] > 0.999).all())
+    assert((a[a['angle_method']=="random"]['KS']['mean'] < 0.05).all())
+    shutil.rmtree(folder)
+
+#test_qpe()