Qubit Visualization in Python | Quantum State Representation | Bloch Sphere – Python

The Bloch sphere provides an intuitive way to understand and visualize the state of a quantum system.

The code generates a random quantum state vector and uses a 3D plot with a Bloch sphere representation to visualize it. 

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Video Explanation

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Steps

1. 1. First, we import the necessary libraries, NumPy and Matplotlib, to work with numerical operations and plotting.
   2. We set the variable num_qubits to the desired number of qubits. This determines the dimensionality of the quantum state vector.
   3. The rand_ket() function generates a random quantum state vector with a specified dimension, which in this case is 2 ** num_qubits. This means the state vector will have a length of 2 for a single qubit (2 ** 1 = 2), 4 for two qubits (2 ** 2 = 4), and so on.
   4. Next, we create a 3D plot using matplotlib.pyplot and specify that it should have a 3D projection using the projection=’3d’ parameter.
   5. We create a Bloch object, which is a part of the qutip library. This object allows us to visualize quantum states on a Bloch sphere representation.
   6. We add the generated state to the Bloch object using the add_states() method. This visualizes the state on the Bloch sphere.
   7. Finally, we call the show() method on the Bloch object to display the 3D plot representing the quantum state.

Python Program

• Method #1

import numpy as np
import matplotlib.pyplot as plt
from qutip import *
# Generate a random quantum state vector
num_qubits = 1  # Number of qubits
state = rand_ket(2 ** num_qubits)

# Visualize the state using a 3D plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
sphere = Bloch(axes=ax)
sphere.add_states(state)
sphere.show()

 

• Method #2

import numpy as np
import matplotlib.pyplot as plt
from qutip import *
# Generate a random quantum state vector
num_qubits = 1  # Number of qubits
state = rand_ket(2 ** num_qubits)

# Alternatively, visualize the state using a Bloch sphere representation
bloch = Bloch()
bloch.add_states(state)
bloch.show()

# Show the plots
plt.show()

 

Output