profiling Module

The pyqpanda3.profiling module provides tools for profiling quantum circuit performance, visualizing circuit features, and generating performance diagrams.

Overview

The profiling module helps analyze quantum circuits from a performance perspective by computing metrics such as connectivity, liveness, parallelism, entanglement, and critical depth. It can generate visual performance profiles and circuit Directed Acyclic Graph (DAG) representations.

Functions

draw_circuit_profile

draw_circuit_profile(circuit: Union[QCircuit, QProg], gate_times: dict,
                     is_show: bool = False, out_file: str = None)

Draws a performance profile of a quantum circuit based on gate execution times. The profile is visualized as a call-graph style diagram similar to profiling tools like gprof.

NOTE

Stdout Output

When is_show=False and out_file is not set, the profile summary is printed to stdout instead of generating a visual graph. The output includes gate timing breakdown and critical path information.

Parameters

Parameter	Type	Default	Description
circuit	QCircuit or QProg	required	The quantum circuit or program to profile
gate_times	dict	required	Dictionary mapping gate names to their execution times
is_show	bool	False	Whether to display the profile graph after drawing
out_file	str	None	File path to save the output image (e.g., "profile.png")

Returns

None. The function generates a visual graph and optionally saves it to a file or displays it.

draw_circuit_DAG

draw_circuit_DAG(circuit: QCircuit, is_show: bool = False,
                 save_fn=None)

Draws the Directed Acyclic Graph (DAG) representation of a quantum circuit, showing the dependency structure between gates organized into layers.

Parameters

Parameter	Type	Default	Description
circuit	QCircuit	required	The quantum circuit to visualize as a DAG
is_show	bool	False	Whether to display the DAG graph after drawing
save_fn	str	None	File path to save the output image

Returns

None. The function generates and optionally saves or displays the DAG visualization.

draw_circuit_features

draw_circuit_features(circuit: Union[QCircuit, QProg], is_show: bool = False,
                      save_fn: str = None, title: str = "")

Computes and visualizes circuit features as a radar chart. The computed features are:

NOTE

Stdout Output

When is_show=False and save_fn is not set, the feature values are printed to stdout as text instead of generating a radar chart. The output includes the numerical scores for each feature.

• Program Communication: Measures how well qubits are connected through gate operations
• Liveness: Fraction of qubits that are actively used throughout the circuit
• Parallelism: Degree of gate-level parallelism in the circuit
• Entanglement: Measure of entanglement capability in the circuit
• Critical Depth: Depth of the critical path relative to total depth

Parameters

Parameter	Type	Default	Description
circuit	QCircuit or QProg	required	The quantum circuit or program to analyze
is_show	bool	False	Whether to display the radar chart
save_fn	str	None	File path to save the radar chart image
title	str	""	Title for the radar chart plot

Returns

None. The function generates and optionally saves or displays the radar chart.

Examples

Profile a circuit with gate timing

from pyqpanda3.profiling import draw_circuit_profile
from pyqpanda3.core import QCircuit, H, CNOT

circuit = QCircuit(3)
circuit << H(0) << CNOT(0, 1) << CNOT(1, 2)

# Define gate execution times (in nanoseconds)
gate_times = {"H": 60, "CNOT": 200}

# Generate and save the performance profile
draw_circuit_profile(circuit, gate_times, out_file="circuit_profile.png")

Visualize circuit features

from pyqpanda3.profiling import draw_circuit_features
from pyqpanda3.core import QCircuit, H, CNOT, RX

circuit = QCircuit(3)
circuit << H(0) << CNOT(0, 1) << RX(1, 1.57) << CNOT(1, 2)

# Generate a radar chart of circuit features
draw_circuit_features(circuit, is_show=True, title="My Circuit")
# Output: Displays a radar chart (5 axes: Program Communication, Liveness, Parallelism, Entanglement, Critical Depth)
draw_circuit_features(circuit, save_fn="features.png", title="Circuit Analysis")

Draw circuit DAG

from pyqpanda3.profiling.profiling import draw_circuit_DAG
from pyqpanda3.core import QCircuit, H, CNOT

circuit = QCircuit(3)
circuit << H(0) << CNOT(0, 1) << CNOT(1, 2)

# Visualize the gate dependency structure
draw_circuit_DAG(circuit, save_fn="circuit_dag.png")

Dependencies

The profiling module requires the following Python packages:

• networkx -- For graph-based circuit analysis
• graphviz -- For rendering DAG and profile visualizations

See Also

• visualization Module -- Circuit drawing and display options
• core Module -- Core quantum computing primitives including QCircuit and QProg