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https://github.com/kristoferssolo/grovers-visualizer.git
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feat: implement grovers search
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62
main.py
62
main.py
@ -9,28 +9,73 @@ using matplotlib.
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from itertools import product
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.axes import Axes
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from qiskit import QuantumCircuit
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from qiskit_aer import AerSimulator
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def grover_search(n: int) -> QuantumCircuit:
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def x(qc: QuantumCircuit, target_state: str) -> None:
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for i, bit in enumerate(reversed(target_state)):
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if bit == "0":
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qc.x(i)
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def ccz(qc: QuantumCircuit, n: int) -> None:
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"""Multi-controlled Z (for 3 qubits, this is a CCZ)"""
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if n == 1:
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qc.z(0)
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elif n == 2:
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qc.cz(0, 1)
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else:
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qc.h(n - 1)
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qc.mcx(list(range(n - 1)), n - 1) # multi-controlled X (Toffoli for 3 qubits)
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qc.h(n - 1)
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def oracule(qc: QuantumCircuit, target_state: str) -> None:
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n = len(target_state)
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x(qc, target_state)
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ccz(qc, n)
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# Undo the X gates
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x(qc, target_state)
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def diffusion(qc: QuantumCircuit, n: int) -> None:
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"""Apply the Grovers diffusion operator"""
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qc.h(range(n))
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qc.x(range(n))
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ccz(qc, n)
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qc.x(range(n))
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qc.h(range(n))
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def grover_search(n: int, target_state: str) -> QuantumCircuit:
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qc = QuantumCircuit(n, n)
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qc.h(range(n))
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num_states = 2**n
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iterations = int(np.floor(np.pi / 4 * np.sqrt(num_states)))
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for _ in range(iterations):
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oracule(qc, target_state)
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diffusion(qc, n)
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qc.measure(range(n), range(n))
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return qc
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def plot_counts(ax: Axes, counts: dict[str, int], target_state: str) -> None:
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"""Create and display a bar chart for the measurement results.
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"""Create and display a bar chart for the measurement results."""
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Parameters:
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counts - A dictionary mapping output states to counts.
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target_state - The target state used in the Grover circuit.
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"""
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# Sort the states (optional: you can sort by state or by count)
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# Sort the states
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states = list(counts.keys())
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frequencies = [counts[s] for s in states]
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@ -52,7 +97,7 @@ def main() -> None:
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plt.ion()
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for state in states:
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qc = grover_search(n_qubits)
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qc = grover_search(n_qubits, state)
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print(qc.draw("text"))
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@ -68,7 +113,6 @@ def main() -> None:
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plot_counts(ax, sorted_counts, state)
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plt.pause(1)
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# plt.ioff() # Do not close automatically
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plt.show()
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