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https://github.com/TheAlgorithms/Python.git
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priya-sundaram-devandChristian Clauss
dde1e49fe1
* quantum: modernize QFT to Qiskit 2.x + re-enable its test quantum/q_fourier_transform.py used the Aer and execute symbols that were removed from qiskit in the 1.0 API break, so it could never run and was on the pytest --ignore list. Port it to the current API: - Drop 'from qiskit import Aer, execute'. Build the circuit unchanged, then simulate with the pure-Python BasicSimulator via transpile() + backend.run(), so no compiled qiskit-aer backend is needed (qiskit-aer has no Python 3.14 wheels yet; BasicSimulator ships inside qiskit core). - Seed the run (seed_simulator=42) and rewrite the doctest to assert the reproducible, shot-noise-independent facts (the four outcomes appear and the counts sum to the shot total) instead of exact per-state counts, which random sampling can never hit. - Add 'qiskit>=2' to project dependencies and drop the quantum ignore + the stale '# TODO: #8818 Re-enable quantum tests' comment in build.yml. Draft until CI confirms qiskit installs and imports on the repo's Python 3.14. * Update quantum/q_fourier_transform.py --------- Co-authored-by: Christian Clauss <cclauss@me.com>
114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
"""
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Build the quantum Fourier transform (QFT) for a desired
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number of qubits using the Qiskit framework.
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This circuit can be used as a building block to design
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Shor's algorithm in quantum computing, as well as
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quantum phase estimation, among others.
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The circuit is simulated with Qiskit's built-in, pure-Python
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``BasicSimulator`` (no compiled ``qiskit-aer`` backend required),
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so it runs anywhere Qiskit itself installs.
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References:
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https://en.wikipedia.org/wiki/Quantum_Fourier_transform
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https://quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.QFT
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"""
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import math
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import numpy as np
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import qiskit
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from qiskit import ClassicalRegister, QuantumCircuit, QuantumRegister, transpile
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from qiskit.providers.basic_provider import BasicSimulator
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def quantum_fourier_transform(number_of_qubits: int = 3) -> qiskit.result.counts.Counts:
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"""
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Build and simulate the quantum Fourier transform applied to the all-zero
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state ``|0...0>``. The QFT maps ``|0...0>`` to a uniform superposition, so
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every computational-basis outcome is (up to shot noise) equally likely.
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# quantum circuit for number_of_qubits = 3:
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┌───┐
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qr_0: ──────■──────────────────────■───────┤ H ├─X─
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│ ┌───┐ │P(π/2) └───┘ │
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qr_1: ──────┼────────■───────┤ H ├─■─────────────┼─
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┌───┐ │P(π/4) │P(π/2) └───┘ │
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qr_2: ┤ H ├─■────────■───────────────────────────X─
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└───┘
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cr: 3/═════════════════════════════════════════════
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Args:
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number_of_qubits : number of qubits
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Returns:
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qiskit.result.counts.Counts: measurement counts over 10,000 shots.
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The simulation is seeded, so the set of observed outcomes is reproducible:
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>>> counts = quantum_fourier_transform(2)
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>>> sorted(counts)
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['00', '01', '10', '11']
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>>> sum(counts.values())
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10000
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>>> quantum_fourier_transform(-1)
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Traceback (most recent call last):
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...
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ValueError: number of qubits must be > 0.
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>>> quantum_fourier_transform('a')
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Traceback (most recent call last):
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...
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TypeError: number of qubits must be a integer.
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>>> quantum_fourier_transform(100)
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Traceback (most recent call last):
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...
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ValueError: number of qubits too large to simulate(>10).
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>>> quantum_fourier_transform(0.5)
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Traceback (most recent call last):
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...
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ValueError: number of qubits must be exact integer.
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"""
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if isinstance(number_of_qubits, str):
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raise TypeError("number of qubits must be a integer.")
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if number_of_qubits <= 0:
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raise ValueError("number of qubits must be > 0.")
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if math.floor(number_of_qubits) != number_of_qubits:
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raise ValueError("number of qubits must be exact integer.")
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if number_of_qubits > 10:
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raise ValueError("number of qubits too large to simulate(>10).")
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qr = QuantumRegister(number_of_qubits, "qr")
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cr = ClassicalRegister(number_of_qubits, "cr")
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quantum_circuit = QuantumCircuit(qr, cr)
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counter = number_of_qubits
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for i in range(counter):
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quantum_circuit.h(number_of_qubits - i - 1)
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counter -= 1
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for j in range(counter):
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quantum_circuit.cp(np.pi / 2 ** (counter - j), j, counter)
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for k in range(number_of_qubits // 2):
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quantum_circuit.swap(k, number_of_qubits - k - 1)
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# measure all the qubits
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quantum_circuit.measure(qr, cr)
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# simulate with 10000 shots on the pure-Python BasicSimulator; seed the run
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# so the observed outcomes are reproducible for the doctest above.
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backend = BasicSimulator()
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transpiled_circuit = transpile(quantum_circuit, backend)
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job = backend.run(transpiled_circuit, shots=10_000, seed_simulator=42)
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return job.result().get_counts(quantum_circuit)
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if __name__ == "__main__":
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print(
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f"Total count for quantum fourier transform state is: \
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{quantum_fourier_transform(3)}"
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)
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