quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT) #15119

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2 changes: 0 additions & 2 deletions .github/workflows/build.yml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
allow-prereleases: true
- run: uv sync --group=test
- name: Run tests
# TODO: #8818 Re-enable quantum tests
run: uv run --with=pytest-run-parallel pytest
--iterations=8 --parallel-threads=auto
--ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
--ignore=machine_learning/lstm/lstm_prediction.py
--ignore=neural_network/input_data.py
--ignore=project_euler/
--ignore=quantum/q_fourier_transform.py
--ignore=scripts/validate_solutions.py
--ignore=web_programming/current_stock_price.py
--ignore=web_programming/fetch_anime_and_play.py
Expand Down
1 change: 1 addition & 0 deletions pyproject.toml
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
"opencv-python>=4.10.0.84",
"pandas>=2.2.3",
"pillow>=11.3",
"qiskit>=2",
"rich>=13.9.4",
"scikit-learn>=1.5.2",
"scipy>=1.16.2",
Expand Down
53 changes: 35 additions & 18 deletions quantum/q_fourier_transform.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
"""
# >>> quantum_fourier_transform(2)
# {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
Build and simulate the quantum Fourier transform applied to the all-zero
state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
every computational-basis outcome is (up to shot noise) equally likely.

# quantum circuit for number_of_qubits = 3:
┌───┐
qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
qr_2: ┤ H ├─■────────■───────────────────────────X─
└───┘
cr: 3/═════════════════════════════════════════════

Args:
n : number of qubits
number_of_qubits : number of qubits

Returns:
qiskit.result.counts.Counts: distribute counts.
qiskit.result.counts.Counts: measurement counts over 10000 shots.

>>> quantum_fourier_transform(2)
{'00': 2500, '01': 2500, '10': 2500, '11': 2500}
The simulation is seeded, so the set of observed outcomes is reproducible:

>>> counts = quantum_fourier_transform(2)
>>> sorted(counts)
['00', '01', '10', '11']
>>> sum(counts.values())
10000
>>> quantum_fourier_transform(-1)
Traceback (most recent call last):
...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

# measure all the qubits
quantum_circuit.measure(qr, cr)
# simulate with 10000 shots
backend = Aer.get_backend("qasm_simulator")
job = execute(quantum_circuit, backend, shots=10000)

# simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
# so the observed outcomes are reproducible for the doctest above.
backend = BasicSimulator()
transpiled_circuit = transpile(quantum_circuit, backend)
job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

return job.result().get_counts(quantum_circuit)

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
Expand Down

1 change: 1 addition & 0 deletions
pyproject.toml

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
Expand Down

53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)#15119

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quantum: modernize QFT to Qiskit 2.x and re-enable its test (DRAFT)
#15119
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2 changes: 0 additions & 2 deletions
.github/workflows/build.yml

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Original file line numberDiff line numberDiff line change
Expand Up@@ -21,7 +21,6 @@ jobs:
 allow-prereleases: true
 - run: uv sync --group=test
 - name: Run tests
 # TODO: #8818 Re-enable quantum tests
 run: uv run --with=pytest-run-parallel pytest
 --iterations=8 --parallel-threads=auto
 --ignore=computer_vision/cnn_classification.py
Expand All@@ -30,7 +29,6 @@ jobs:
 --ignore=machine_learning/lstm/lstm_prediction.py
 --ignore=neural_network/input_data.py
 --ignore=project_euler/
 --ignore=quantum/q_fourier_transform.py
 --ignore=scripts/validate_solutions.py
 --ignore=web_programming/current_stock_price.py
 --ignore=web_programming/fetch_anime_and_play.py
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1 change: 1 addition & 0 deletions
pyproject.toml

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Expand Up@@ -21,6 +21,7 @@ dependencies = [
 "opencv-python>=4.10.0.84",
 "pandas>=2.2.3",
 "pillow>=11.3",
 "qiskit>=2",
 "rich>=13.9.4",
 "scikit-learn>=1.5.2",
 "scipy>=1.16.2",
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53 changes: 35 additions & 18 deletions
quantum/q_fourier_transform.py

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@@ -1,27 +1,34 @@
"""
Build the quantum fourier transform (qft) for a desire
number of quantum bits using Qiskit framework. This
experiment run in IBM Q simulator with 10000 shots.
This circuit can be use as a building block to design
the Shor's algorithm in quantum computing. As well as,
quantum phase estimation among others.
.
Build the quantum Fourier transform (QFT) for a desired
number of qubits using the Qiskit framework.

This circuit can be used as a building block to design
Shor's algorithm in quantum computing, as well as
quantum phase estimation, among others.

The circuit is simulated with Qiskit's built-in, pure-Python
``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
so it runs anywhere Qiskit itself installs.

References:
https://en.wikipedia.org/wiki/Quantum_Fourier_transform
https://qiskit.org/textbook/ch-algorithms/quantum-fourier-transform.html
https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
"""

import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
from qiskit.providers.basic_provider import BasicSimulator


def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
 """
 # >>> quantum_fourier_transform(2)
 # {'00': 2500, '01': 2500, '11': 2500, '10': 2500}
 Build and simulate the quantum Fourier transform applied to the all-zero
 state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
 every computational-basis outcome is (up to shot noise) equally likely.

 # quantum circuit for number_of_qubits = 3:
 ┌───┐
 qr_0: ──────■──────────────────────■───────┤ H ├─X─
Expand All@@ -31,13 +38,20 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts
 qr_2: ┤ H ├─■────────■───────────────────────────X─
 └───┘
 cr: 3/═════════════════════════════════════════════

 Args:
 n : number of qubits
 number_of_qubits : number of qubits

 Returns:
 qiskit.result.counts.Counts: distribute counts.
 qiskit.result.counts.Counts: measurement counts over 10000 shots.

 >>> quantum_fourier_transform(2)
 {'00': 2500, '01': 2500, '10': 2500, '11': 2500}
 The simulation is seeded, so the set of observed outcomes is reproducible:

 >>> counts = quantum_fourier_transform(2)
 >>> sorted(counts)
 ['00', '01', '10', '11']
 >>> sum(counts.values())
 10000
 >>> quantum_fourier_transform(-1)
 Traceback (most recent call last):
 ...
Expand DownExpand Up@@ -82,9 +96,12 @@ def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts

 # measure all the qubits
 quantum_circuit.measure(qr, cr)
 # simulate with 10000 shots
 backend = Aer.get_backend("qasm_simulator")
 job = execute(quantum_circuit, backend, shots=10000)

 # simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
 # so the observed outcomes are reproducible for the doctest above.
 backend = BasicSimulator()
 transpiled_circuit = transpile(quantum_circuit, backend)
 job = backend.run(transpiled_circuit, shots=10000, seed_simulator=42)

 return job.result().get_counts(quantum_circuit)

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