Visualization in scqubits includes energy level diagrams, parameter sweeps, and state representations.

from scqubits import Transmon
import matplotlib.pyplot as plt
 
transmon = Transmon(EJ=15.0, EC=0.3, ncut=30)
 
# Energy level diagram
fig, ax = plt.subplots()
evals = transmon.eigenvals(n=6)
ax.hlines(evals, 0, 1, colors='b')
for i, E in enumerate(evals):
    ax.text(1.05, E, f'|{i}⟩', va='center')
ax.set_xlim(0, 1.5)
ax.set_ylabel('Energy (GHz)')
ax.set_title('Transmon Energy Levels')
plt.show()
 
# Potential and wavefunctions
phi = np.linspace(-np.pi, np.pi, 200)
V = -15.0 * np.cos(phi)  # Josephson potential
ax.plot(phi, V, 'k-', label='V(φ)')
 
# Overlay wavefunctions (heuristic)
for i in range(3):
    evec = transmon.eigenvecs(n=i+1)[i]
    # (Plotting eigenvectors in phase basis requires basis transformation)
ax.legend()
plt.show()

scqubits integrates with matplotlib for standard plots. For complex visualizations, extract matrices and use custom plotting code.

Visualization helps debug designs: identify unwanted level crossings, check convergence, compare parameter effects.