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API Reference: qbraid.visualization

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Draw Circuit Diagrams

qBraid’s circuit_drawer function takes in any type of supported quantum circuit and draws the corresponding visualization. Here’s an example using braket and cirq: 
from qbraid import random_circuit
from qbraid.visualization import circuit_drawer

circuit = random_circuit("braket")
circuit_drawer(circuit)
# T  : |0| 1 |2|
#
# q0 : -C-C---S-
#       | |
# q1 : -Z-|-Z---
#         |
# q2 : -Z-X-----
#
# T  : |0| 1 |2|

circuit = random_circuit("cirq")
circuit_drawer(circuit)
# 0: ───────────×───Z───
#               │
# 1: ───iSwap───×───────
#       │
# 2: ───iSwap───Y───H───

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Draw OpenQASM 3 circuits

The qasm3_drawer takes in a string of OpenQASM3 code and outputs an ASCII representation of the described circuit. 
from qbraid.visualization import qasm3_drawer

program = """
OPENQASM 3;
include "stdgates.inc";
qubit[2] q;
h q[0];
cx q[0], q[1];
"""

qasm3_drawer(program)
#      |---|
# q0---| h |-----■---
#      |---|     |
#             |----|
# q1----------| cx |-
#             |----|

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Plot Experimental Results

Gather the measurement counts and plot the histogram data for any Result constructed from ResultData of type qbraid.runtime.GateModelResultData: 
from qbraid.visualization import plot_histogram

counts = result.data.get_counts()
# {'00': 483, '01': 14, '10': 486, '11': 17}

plot_histogram(counts)
Or, using the same measurement counts data, plot the probability distribution: 
from qbraid.visualization plot_distribution

plot_distribution(counts)
Or, plot a batch of measurement counts for any list[qbraid.runtime.GateModeResultData]: 
batch_jobs = device.run_batch([circuit0, circuit1], shots=1000)

batch_results = [job.result() for job in batch_jobs]

batch_counts = [result.data.get_counts() for result in batch_results]
# e.g. [{'0': 136, '1': 864}, {'0': 166, '1': 834}]

plot_histogram(batch_counts)
Using the qBraid runtime job and results primitives, experimental data is returned in a standardized format, facilitating straightforward comparisons and benchmarking of results across different providers and backends.

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Plot Transpiler Conversions

Plot all supported conversions between registered program types available through the qbraid.transpiler: 
from qbraid import ConversionGraph
from qbraid.visualization import plot_conversion_graph

graph = ConversionGraph()

plot_conversion_graph(graph, legend=True)

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Plot Runtime Conversion Scheme

Plot the runtime conversion scheme for any qbraid.runtime.QuantumDevice. The QuantumDevice.scheme defines the qbraid.transpiler.ConversionScheme that will be used to carry out the “transpile” step of the Quantum Job Submission Process. 
from qbraid import QbraidProvider
from qbraid.visualization import plot_runtime_conversion_scheme

provider = QbraidProvider()

device = provider.get_device("qbraid_qir_simulator")

print(device.scheme)
By setting the maximum path depth to 1, we ensure the ConversionGraph includes only program types (nodes) that are directly connected to the target program type by a single conversion step (edge). 
device.update_scheme(max_path_depth=1)

plot_runtime_conversion_scheme(device)
By setting the maximum path depth to 2, we can see how the ConversionGraph is expanded to include program types (nodes) that are at most two conversion steps (edges) away from the target program type. 
device.update_scheme(max_path_depth=2)

plot_runtime_conversion_scheme(device)