Parameter sweeps compute qubit properties (frequencies, anharmonicity, matrix elements) as a function of design parameters. Useful for optimizing qubit design and understanding parameter sensitivity.

from scqubits import Transmon, Fluxonium
import numpy as np
import matplotlib.pyplot as plt
 
# Sweep EJ
EJ_values = np.linspace(10, 30, 50)
f_01_values = []
anh_values = []
 
for EJ in EJ_values:
    transmon = Transmon(EJ=EJ, EC=0.3, ncut=30)
    f_01_values.append(transmon.f_01())
    anh_values.append(transmon.anharmonicity())
 
# Plot
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(EJ_values, f_01_values)
ax1.set_xlabel('E_J (GHz)')
ax1.set_ylabel('f_01 (GHz)')
ax2.plot(EJ_values, anh_values)
ax2.set_xlabel('E_J (GHz)')
ax2.set_ylabel('Anharmonicity (GHz)')
plt.show()
 
# Sweep flux (for fluxonium)
fluxes = np.linspace(0, 1, 100)
f_01_flux = []
for phi in fluxes:
    fluxonium = Fluxonium(EJ=12.0, EC=2.5, EL=0.5, flux=phi, ncut=30)
    f_01_flux.append(fluxonium.f_01())
 
ax1.plot(fluxes, f_01_flux)
ax1.set_xlabel('Flux (Φ/Φ_0)')

Parameter sweeps reveal design trade-offs: increasing $E_J$ increases frequency but may reduce anharmonicity. Flux sweeps show tunability of fluxoniums.