This browser is no longer supported.
Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support.
![](data:image/svg+xml, %3Csvg xmlns='http://www.w3.org/2000/svg' height='64' class='font-weight-bold' style='font: 600 30.11764705882353px "SegoeUI", Arial' width='64'%3E%3Ccircle fill='hsl(134.4, 71%, 23%)' cx='32' cy='32' r='32' /%3E%3Ctext x='50%25' y='55%25' dominant-baseline='middle' text-anchor='middle' fill='%23FFF' %3ENC%3C/text%3E%3C/svg%3E)
I have this code: host.py
import json
import numpy as np
import matplotlib.pyplot as plt
import qsharp
import qsharp.azure
plt.style.use('ggplot')
from qsharp import azure
from azure.quantum import Workspace
from azure.quantum.optimization import Problem, Solver
import time
from Microsoft.Quantum.Samples import TrainHalfMoonModel
# Azure Quantum workspace information
subscription_id = "ID1"
resource_group = "RG"
workspace_name = "THEWORKSPACENAME"
location = "THELOCATION"
workspace = Workspace(subscription_id=subscription_id, resource_group=resource_group, name=workspace_name, location=location)
targets = qsharp.azure.connect(
resourceId = "THERESOURCEIT",
location = "THELOCATION")
# Directly set the Azure Quantum target
qsharp.azure.target("microsoft.estimator")
def train_and_classify(file_path):
with open(file_path) as f:
data = json.load(f)
start_time = time.time()
parameter_starting_points = [
[0.060057, 3.00522, 2.03083, 0.63527, 1.03771, 1.27881, 4.10186, 5.34396],
[0.586514, 3.371623, 0.860791, 2.92517, 1.14616, 2.99776, 2.26505, 5.62137],
[1.69704, 1.13912, 2.3595, 4.037552, 1.63698, 1.27549, 0.328671, 0.302282],
[5.21662, 6.04363, 0.224184, 1.53913, 1.64524, 4.79508, 1.49742, 1.545]
]
# Submit the Q# operation to Azure Quantum
trainingVectors=data['TrainingData']['Features']
trainingLabels=data['TrainingData']['Labels']
initialParameters=parameter_starting_points
floral = [row[0] for row in trainingVectors]
bees = [row[1] for row in trainingVectors]
val1 = [row[0] for row in initialParameters]
val2 = [row[1] for row in initialParameters]
val3 = [row[3] for row in initialParameters]
val4 = [row[4] for row in initialParameters]
# Quantum computer
try:
parameters = qsharp.azure.execute(TrainHalfMoonModel, floral=floral, bees=bees, trainingLabels=trainingLabels, _1=val1, _2=val2, _3=val3, _4=val4, shots=1, qubits=8, jobName=f"Train the model with {file_path}", timeout=3600)
except Exception as e:
print(f"Got error: {e}")
latest_job = max(workspace.list_jobs(), key=lambda job: job.details.creation_time)
while latest_job.details.status == "Executing":
time.sleep(10)
latest_job.refresh()
print(f"Current job status: {latest_job.details.status}")
# Regular computer
#(parameters) = TrainHalfMoonModel(
#floral=floral, bees=bees, trainingLabels=trainingLabels, _1=val1, _2=val2, _3=val3, _4=val4
#)
end_time = time.time()
training_time = (end_time - start_time)
rows = len(data['TrainingData']['Features'])
print(f"Training completed for {file_path}. Rows: {rows}. Time taken: {training_time} seconds")
return rows, training_time
if __name__ == "__main__":
json_folder = "jsons"
files = ["data1.json", "data2.json", "data3.json", "data4.json", "data5.json", "data6.json", "data7.json", "data8.json", "data9.json", "data10.json"]
amount_tasks = []
training_times = []
for file in files:
time.sleep(10)
file_path = f"{json_folder}/{file}"
tasks, time_taken = train_and_classify(file_path)
amount_tasks.append(tasks)
training_times.append(time_taken)
# Plotting the chart
plt.plot(training_times, amount_tasks, marker='o', linestyle='-')
plt.ylabel('Amount of Rows')
plt.xlabel('Time (seconds)')
plt.title('Training Time vs Amount of Rows')
plt.show()
And Training.qs
namespace Microsoft.Quantum.Samples {
open Microsoft.Quantum.Convert;
open Microsoft.Quantum.Intrinsic;
open Microsoft.Quantum.Canon;
open Microsoft.Quantum.Arrays;
open Microsoft.Quantum.MachineLearning;
open Microsoft.Quantum.Math;
function WithProductKernel(scale : Double, sample : Double[]) : Double[] {
return sample + [scale * Fold(TimesD, 1.0, sample)];
}
function Preprocessed(samples : Double[][]) : Double[][] {
let scale = 1.0;
return Mapped(
WithProductKernel(scale, _),
samples
);
}
function DefaultSchedule(samples : Double[][]) : SamplingSchedule {
return SamplingSchedule([
0..Length(samples) - 1
]);
}
function ClassifierStructure() : ControlledRotation[] {
return [
ControlledRotation((0, new Int[0]), PauliX, 4),
ControlledRotation((0, new Int[0]), PauliZ, 5),
ControlledRotation((1, new Int[0]), PauliX, 6),
ControlledRotation((1, new Int[0]), PauliZ, 7),
ControlledRotation((0, [1]), PauliX, 0),
ControlledRotation((1, [0]), PauliX, 1),
ControlledRotation((1, new Int[0]), PauliZ, 2),
ControlledRotation((1, new Int[0]), PauliX, 3)
];
}
function CombineFunction(a: Double, b: Double): Double[] {
return [a, b];
}
function CombineFunction4(a: Double, b: Double, c: Double, d: Double): Double[] {
return [a, b, c, d];
}
operation TrainHalfMoonModel(
floral : Double[],
bees : Double[],
trainingLabels : Int[],
_1 : Double[],
_2 : Double[],
_3 : Double[],
_4 : Double[],
//) : (Double[], Double) {
) : (Double[]) {
let trainingVectorsArray = Zipped(floral, bees);
let trainingVectors = Mapped(CombineFunction, trainingVectorsArray);
let initialParametersArray = Zipped4(_1, _2, _3, _4);
let initialParameters = Mapped(CombineFunction4, initialParametersArray);
let samples = Mapped(LabeledSample,Zipped(Preprocessed(trainingVectors), trainingLabels));
let (optimizedModel, nMisses) = TrainSequentialClassifier(Mapped(SequentialModel(ClassifierStructure(), _, 0.0), initialParameters),
samples,
DefaultTrainingOptions()
w/ LearningRate <- 0.1
w/ MinibatchSize <- 15
w/ Tolerance <- 0.005
w/ NMeasurements <- 10000
w/ MaxEpochs <- 16,
DefaultSchedule(trainingVectors),
DefaultSchedule(trainingVectors)
);
//return (optimizedModel::Parameters, optimizedModel::Bias);
return (optimizedModel::Parameters);
}
I am connecting to Azure Quantum Workspace. As you have spotted, I am training the classification MLmodel HalfMoon with data like this:
{
"TrainingData": {
"Features": [
[
86091.0,
58.0
],
[
98578.0,
134.0
],
[
34851.0,
34.0
],
[
26760.0,
490.0
],
[
36580.0,
460.0
],
[
24205.0,
441.0
],
[
44634.0,
302.0
],
[
40966.0,
274.0
],
[
54651.0,
148.0
],
[
28153.0,
263.0
],
[
12008.0,
249.0
],
[
94799.0,
30.0
],
[
84764.0,
205.0
],
[
69071.0,
461.0
],
[
46563.0,
486.0
],
[
22630.0,
56.0
],
[
38962.0,
279.0
],
[
92771.0,
377.0
],
[
22934.0,
306.0
],
[
48731.0,
73.0
],
[
16857.0,
294.0
],
[
11091.0,
32.0
],
[
72222.0,
281.0
],
[
11591.0,
131.0
],
[
30883.0,
207.0
],
[
43499.0,
484.0
],
[
69419.0,
150.0
],
[
55893.0,
145.0
],
[
89817.0,
165.0
],
[
55651.0,
329.0
],
[
56986.0,
117.0
],
[
95541.0,
351.0
],
[
63641.0,
212.0
],
[
42210.0,
74.0
],
[
69868.0,
381.0
],
[
82337.0,
138.0
],
[
62307.0,
363.0
],
[
91318.0,
462.0
],
[
80484.0,
307.0
],
[
71914.0,
105.0
],
[
25492.0,
109.0
],
[
99223.0,
452.0
],
[
19260.0,
496.0
],
[
63313.0,
121.0
],
[
71126.0,
438.0
],
[
83397.0,
127.0
],
[
38562.0,
41.0
],
[
47829.0,
369.0
],
[
30291.0,
289.0
],
[
68468.0,
276.0
],
[
83562.0,
234.0
],
[
86920.0,
347.0
],
[
57956.0,
171.0
],
[
39641.0,
267.0
],
[
15517.0,
354.0
],
[
18606.0,
97.0
],
[
42499.0,
339.0
],
[
52468.0,
134.0
],
[
55145.0,
355.0
],
[
69510.0,
138.0
],
[
87156.0,
201.0
],
[
39751.0,
406.0
],
[
54010.0,
401.0
],
[
35248.0,
438.0
],
[
28351.0,
432.0
],
[
56506.0,
25.0
],
[
46459.0,
255.0
],
[
14901.0,
9.0
],
[
39117.0,
141.0
],
[
72828.0,
479.0
],
[
5881.0,
463.0
],
[
82888.0,
285.0
],
[
75860.0,
482.0
],
[
91955.0,
15.0
],
[
7376.0,
444.0
],
[
16578.0,
135.0
],
[
71147.0,
238.0
],
[
32673.0,
248.0
],
[
52579.0,
355.0
],
[
12145.0,
172.0
],
[
31105.0,
399.0
],
[
20850.0,
229.0
],
[
5485.0,
385.0
],
[
62891.0,
24.0
],
[
13703.0,
382.0
],
[
22394.0,
384.0
],
[
50084.0,
500.0
],
[
11557.0,
155.0
],
[
70083.0,
12.0
],
[
22979.0,
455.0
],
[
75102.0,
453.0
],
[
10949.0,
59.0
],
[
70348.0,
61.0
],
[
43891.0,
78.0
],
[
75237.0,
69.0
],
[
47461.0,
114.0
],
[
71399.0,
212.0
],
[
93535.0,
199.0
],
[
46460.0,
106.0
],
[
42635.0,
48.0
]
],
"Labels": [
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1
]
},
"ValidationData": {
"Features": [
[
35314.0,
315.0
],
[
43170.0,
410.0
]
],
"Labels": [
0,
0
]
}
}
the problem is that I receive this error - Job was terminated due to a failed assertion
I had to finish the project, that's why I have this code to bypass the errors. I have no Idea why, but I always have 240 seconds on executing the training and I cant change that
except Exception as e
But the new error FailedAssertion still occurs. How can I resolve it? Thanks!
I suppose we can close this treat. I just found out microsoft released v1 of SDK for Quantum Computing - that means I have to rewrite the whole code