qiskit/qft.py

This is an example of the qiskit.circuit.library.QFT implementation of the Quantum Fourier transform function which is documented at: docs.quantum.ibm.com/api/qiskit/0.44/qiskit.circuit.library.QFT

Output:

init: [1, 0, 0, 0, 0, 0, 0, 0]
qc
     ┌──────────────────────────────┐┌──────┐
q_0: ┤0                             ├┤0     ├
     │                              ││      │
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├┤1 QFT ├
     │                              ││      │
q_2: ┤2                             ├┤2     ├
     └──────────────────────────────┘└──────┘
transpiled qc
     ┌──────────────────────────────┐                                     ┌───┐   
q_0: ┤0                             ├────────────────────■────────■───────┤ H ├─X─
     │                              │              ┌───┐ │        │P(π/2) └───┘ │ 
q_1: ┤1 Initialize(1,0,0,0,0,0,0,0) ├──────■───────┤ H ├─┼────────■─────────────┼─
     │                              │┌───┐ │P(π/2) └───┘ │P(π/4)                │ 
q_2: ┤2                             ├┤ H ├─■─────────────■──────────────────────X─
     └──────────────────────────────┘└───┘
Statevector([0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
             0.35355339+0.j, 0.35355339+0.j, 0.35355339+0.j,
             0.35355339+0.j, 0.35355339+0.j],
            dims=(2, 2, 2))

init: [0.0, 0.35355339059327373, 0.5, 0.3535533905932738, 6.123233995736766e-17, -0.35355339059327373, -0.5, -0.35355339059327384]
Statevector([ 7.71600526e-17+5.22650714e-17j,
              1.86749130e-16+7.07106781e-01j,
             -6.10667421e-18+6.10667421e-18j,
              1.13711443e-16-1.11022302e-16j,
              2.16489014e-17-8.96726857e-18j,
             -5.68557215e-17-1.11022302e-16j,
             -6.10667421e-18-4.94044770e-17j,
             -3.30200457e-16-7.07106781e-01j],
            dims=(2, 2, 2))

So this also serves as a more interesting example of quantum compilation, mapping the QFT gate to Qiskit Aer primitives.

If we don't transpile in this example, then running blows up with:

qiskit_aer.aererror.AerError: 'unknown instruction: QFT'

The second input is:

x_{i} = \frac{1}{2} s i n (2 * π * i / 8)

(19)

and the output of that approximately:

[0, 1j/sqrt(2), 0, 0, 0, 0, 0, 1j/sqrt(2)]

which can be defined simply as the normalized DFT of the input quantum state vector.

From this we see that the Quantum Fourier transform is equivalent to a direct discrete Fourier transform on the quantum state vector, related: physics.stackexchange.com/questions/110073/how-to-derive-quantum-fourier-transform-from-discrete-fourier-transform-dft

qiskit/qft.py

#!/usr/bin/env python

import math

from qiskit import QuantumCircuit, transpile
from qiskit.circuit.library import QFT
from qiskit_aer import Aer, AerSimulator

n = 3
N = 2**n

def test(init, print_qc=False):
    qc = QuantumCircuit(n)
    qc.initialize(init)
    qft = QFT(num_qubits=n).to_gate()
    qc.append(qft, qargs=range(3))
    print(f'init: {init}')
    if print_qc:
        print('qc')
        print(qc)
    qc = transpile(qc, AerSimulator())
    if print_qc:
        print('transpiled qc')
        print(qc)
    print(Aer.get_backend('statevector_simulator').run(qc, shots=1).result().get_statevector())
    print()

test([1] + [0] * (N - 1), print_qc=True)
test([math.sin(i * 2 * math.pi / N)/2 for i in range(N)])

qiskit/qft.py

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