---
# System prepended metadata

title: 'Heatmaps: A Powerful Visualization Tool'

---

# Heatmaps: A Powerful Visualization Tool

Heatmaps are powerful visualization tools for displaying data in various fields, including statistics, machine learning, and engineering. They're useful for analyzing relationships between different variables or for visualizing large datasets.

## Creating Heatmaps in Python

Python offers libraries for creating 2D, 3D, and 4D heatmaps, such as **Matplotlib**, **Seaborn**, and **Plotly**.

### 2D Heatmaps

Using **Seaborn**:

```Python
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

data = np.random.rand(10, 12)
ax = sns.heatmap(data)
plt.show()
```

### 3D Heatmaps

Using **Plotly**:

```Python
import plotly.express as px
import numpy as np

data = np.random.rand(10, 10, 10)
fig = px.imshow(data, animation_frame=0, z=1)
fig.show()
```

### 4D Heatmaps

Using **Plotly**:

```Python
import plotly.graph_objs as go
import numpy as np

data = np.random.rand(10, 10, 10, 3)
fig = go.Figure(go.Volume(
    x=data[..., 0].flatten(),
    y=data[..., 1].flatten(),
    z=data[..., 2].flatten(),
    value=data[..., 3].flatten(),
    isomin=0.1,
    isomax=0.9,
    opacity=0.1,
    surface_count=17,
))

fig.show()
```

## Adapting Heatmaps for Quantum Computations with Qiskit

To create a heatmap visualization of the results obtained from a quantum circuit using **Qiskit**, follow these steps:

1. Install **Qiskit** and necessary libraries:

```bash
pip install qiskit seaborn numpy matplotlib
```

2. Create a quantum circuit and execute it on a simulator or real hardware:

```Python
from qiskit import QuantumCircuit, Aer, transpile, assemble
from qiskit.visualization import plot_histogram

qc = QuantumCircuit(2, 2)
qc.h(0)
qc.cx(0, 1)
qc.measure([0, 1], [0, 1])

backend = Aer.get_backend('qasm_simulator')
tqc = transpile(qc, backend)
qobj = assemble(tqc)
result = backend.run(qobj).result()
```

3. Convert the results into a heatmap-compatible format:

```Python
counts = result.get_counts()
heatmap_data = [[0 for _ in range(2)] for _ in range(2)]

for key, value in counts.items():
    x, y = [int(i) for i in key]
    heatmap_data[x][y] = value
```

4. Visualize the heatmap using **Seaborn**:

```Python
import seaborn as sns
import matplotlib.pyplot as plt

ax = sns.heatmap(heatmap_data, annot=True, fmt="d", cmap="YlGnBu")
plt.show()
```

This code will create a heatmap visualization of the results obtained from a quantum circuit using **Qiskit** and **Seaborn**.
