---
title: Visualize results
description: Plot quantum circuit execution results using Qiskit
source: https://quantum.cloud.ibm.com/docs/en/guides/visualize-results
---

# Visualize results

### Package versions

The code on this page was developed using the following requirements.
We recommend using these versions or newer.

```
qiskit[all]~=2.5.1
```

## Plot histogram

The `plot_histogram` function visualizes the result of sampling a quantum circuit on a QPU.

> **Using the output from functions**
>
> This function returns a `matplotlib.Figure` object. When the last line of a code cell outputs these objects, Jupyter notebooks display them below the cell. If you call these functions in some other environments or in scripts, you will need to explicitly show or save the outputs.
>
> Two options are:
>
> - Call `.show()` on the returned object to open the image in a new window (assuming your configured matplotlib backend is interactive).
> - Call `.savefig("out.png")` to save the figure to `out.png` in the current working directory. The `savefig()` method takes a path so you can adjust the location and filename where you're saving the output. For example, `plot_state_city(psi).savefig("out.png")`.

For example, make a two-qubit Bell state:

```python
from qiskit.primitives import StatevectorSampler as Sampler
from qiskit.transpiler import generate_preset_pass_manager

from qiskit.circuit import QuantumCircuit
from qiskit.visualization import plot_histogram
```

```python
# Quantum circuit to make a Bell state
bell = QuantumCircuit(2)
bell.h(0)
bell.cx(0, 1)
bell.measure_all()

pm = generate_preset_pass_manager(optimization_level=1)
isa_circuit = pm.run(bell)

# execute the quantum circuit
sampler = Sampler()
job = sampler.run([isa_circuit])
result = job.result()

print(result)
```

Output:

```
PrimitiveResult([SamplerPubResult(data=DataBin(meas=BitArray(<shape=(), num_shots=1024, num_bits=2>)), metadata={'shots': 1024, 'circuit_metadata': {}})], metadata={'version': 2})
```

```python
plot_histogram(result[0].data.meas.get_counts())
```

Output:

![Output of the previous code cell](https://quantum.cloud.ibm.com/docs/images/guides/visualize-results/extracted-outputs/57d8053e-d030-460d-9c1f-772e53b1a49b-0.svg)

### Options when plotting a histogram

Use the following options for `plot_histogram` to adjust the output graph.

- `legend`: Provides a label for the executions. It takes a list of strings used to label each execution's results. This is mostly useful when plotting multiple execution results in the same histogram
- `sort`: Adjusts the order of the bars in the histogram. It can be set to either ascending order with `asc` or descending order with `desc`
- `number_to_keep`: Takes an integer for the number of terms to show. The rest are grouped together in a single bar called "rest"
- `color`: Adjusts the color of the bars; takes a string or a list of strings for the colors to use for the bars for each execution
- `bar_labels`: Adjusts whether labels are printed above the bars
- `figsize`: Takes a tuple of the size in inches to make the output figure

```python
# Execute two-qubit Bell state again

job = sampler.run([isa_circuit], shots=1000)
second_result = job.result()

# Plot results with custom options
plot_histogram(
    [
        result[0].data.meas.get_counts(),
        second_result[0].data.meas.get_counts(),
    ],
    legend=["first", "second"],
    sort="desc",
    figsize=(15, 12),
    color=["orange", "black"],
    bar_labels=False,
)
```

Output:

![Output of the previous code cell](https://quantum.cloud.ibm.com/docs/images/guides/visualize-results/extracted-outputs/bd70e13f-5c52-42fb-8dde-980b15e3604a-0.svg)

## Plot Estimator results

Qiskit does not have a built-in function for plotting Estimator results, but you can use Matplotlib's [`bar` plot](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) for a quick visualization.

To demonstrate, the following cell estimates the expectation values of seven different observables on a quantum state.

```python
import numpy as np
from qiskit import QuantumCircuit
from qiskit.quantum_info import SparsePauliOp
from qiskit.primitives import StatevectorEstimator as Estimator
from qiskit.transpiler import generate_preset_pass_manager
from matplotlib import pyplot as plt

# Simple estimation experiment to create results
qc = QuantumCircuit(2)
qc.h(0)
qc.crx(1.5, 0, 1)

observables_labels = ["ZZ", "XX", "YZ", "ZY", "XY", "XZ", "ZX"]
observables = [SparsePauliOp(label) for label in observables_labels]

pm = generate_preset_pass_manager(optimization_level=1)
isa_circuit = pm.run(qc)
isa_observables = [
    operator.apply_layout(isa_circuit.layout) for operator in observables
]

# Reshape observable array for broadcasting
reshaped_ops = np.fromiter(isa_observables, dtype=object)
reshaped_ops = reshaped_ops.reshape((7, 1))

estimator = Estimator()
job = estimator.run([(isa_circuit, reshaped_ops)])
result = job.result()[0]
exp_val = job.result()[0].data.evs
print(result)

# Since the result array is structured as a 2D array where each element is a
# list containing a single value, you need to flatten the array.

# Plot using Matplotlib
plt.bar(observables_labels, exp_val.flatten())
```

Output:

```
PubResult(data=DataBin(evs=np.ndarray(<shape=(7, 1), dtype=float64>), stds=np.ndarray(<shape=(7, 1), dtype=float64>), shape=(7, 1)), metadata={'target_precision': 0.0, 'circuit_metadata': {}})
```

```
<BarContainer object of 7 artists>
```

![Output of the previous code cell](https://quantum.cloud.ibm.com/docs/images/guides/visualize-results/extracted-outputs/17c9893a-d1bf-4726-b444-6dce1d56805f-2.svg)

The following cell uses the estimated [standard error](https://en.wikipedia.org/wiki/Standard_error) of each result and adds them as error bars. See the [`bar` plot documentation](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html) for a full description of the plot.

```python
standard_error = job.result()[0].data.stds

_, ax = plt.subplots()
ax.bar(
    observables_labels,
    exp_val.flatten(),
    yerr=standard_error.flatten(),
    capsize=2,
)
ax.set_title("Expectation values (with standard errors)")
```

Output:

```
Text(0.5, 1.0, 'Expectation values (with standard errors)')
```

![Output of the previous code cell](https://quantum.cloud.ibm.com/docs/images/guides/visualize-results/extracted-outputs/4eb79f4b-36b5-4797-a1a0-67d881d46ca4-1.svg)