Visualization

The pyqpanda3.visualization module provides tools for visualizing quantum states, circuits, and probability distributions using matplotlib, including Bloch sphere plots, state vector diagrams, probability bar charts, and circuit drawings with statistics and ASCII art. It also provides instruction-schedule visualization (draw_time_scheduling()) to render the scheduled instruction stream produced by QProg.to_instruction() as a timeline figure.

Import

from pyqpanda3 import visualization
# or
import pyqpanda3.visualization

Sub-topics

Topic	Description
Bloch Sphere	Bloch sphere plotting functions
Quantum State	State vector and density matrix visualization
Probability	Probability distribution plotting
Circuit	Circuit drawing, statistics, and ASCII art
Time Scheduling	draw_time_scheduling(), parse_instruction_schedule(), and other schedule visualization

The .draw() Method

After importing the visualization module, QProg and QCircuit objects gain a .draw() convenience method for quick circuit rendering:

from pyqpanda3 import visualization
from pyqpanda3.core import QCircuit, H, CNOT

visualization  # triggers the monkey-patch

circuit = QCircuit(2)
circuit << H(0) << CNOT(0, 1)
print(circuit.draw())

Output:

          ┌─┐        
q_0:  |0>─┤H├ ───*── 
          └─┘ ┌──┴─┐ 
q_1:  |0>──── ┤CNOT├ 
              └────┘ 
 c :   / ═══════════

Example

from pyqpanda3 import visualization
from pyqpanda3.core import CPUQVM, QProg, H, CNOT, measure

# Build and run a Bell-state program
prog = QProg()
prog << H(0) << CNOT(0, 1) << measure(0, 0) << measure(1, 1)
qvm = CPUQVM()
qvm.run(prog, 1000)
state = qvm.result().get_state_vector()

# Plot the resulting quantum state
fig = visualization.plot_state(state)
print(type(fig).__name__)