Documentation¶
quchip is an open-source Python toolkit for modeling superconducting quantum chips.
A predictive chip model needs more than a Hamiltonian: device physics, control-line transformations, frames and approximations, dissipation, and measured observables all belong to it. quchip represents each part explicitly. Declare the chip once; the same declaration drives dressed-state analysis, model reduction, control sequencing, open-system simulation, parameter sweeps, and exact JAX gradients.
Install¶
quchip requires Python 3.11 or newer.
pip install quchip
Optional extras: quchip[dynamiqs] for the JAX-native backend, quchip[viz] for graph visualization, quchip[scqubits] for scqubits interoperability.
A minimal chip¶
import numpy as np
from quchip import (
Capacitive, ChargeDrive, Chip, DuffingTransmon,
Gaussian, QuantumSequence, Resonator,
)
qubit = DuffingTransmon(freq=5.24, anharmonicity=-0.26, levels=3)
readout = Resonator(freq=6.65, levels=4)
chip = Chip(
[qubit, readout],
couplings=[Capacitive(qubit, readout, g=0.060)],
frame="rotating",
)
drive = ChargeDrive(qubit)
chip.wire(drive)
sequence = QuantumSequence(chip)
sequence.schedule(
drive,
envelope=Gaussian(duration=40.0, amplitude=0.030),
freq=chip.freq(qubit),
)
result = sequence.simulate(
tlist=np.linspace(0.0, 40.0, 81),
initial_state=chip.state({qubit: 0, readout: 0}),
e_ops={qubit: qubit.projector(1, 1)},
)
print(float(result.expect_final(qubit).real))
fig = result.plot_populations(trace_out=readout)
fig.savefig("populations.png", dpi=200)
The pulse carrier comes from the dressed chip frequency; the printed value is the excited-state population after a nominal π pulse. The last two lines plot the qubit populations with the readout resonator traced out — the figure below is the saved output of the snippet.
quchip uses GHz for ordinary frequencies, ns for time, and mK for temperature. The implemented conventions and approximations are recorded in the physics reference.
Worked examples and guides are being added incrementally.