qutip

name	qutip
description	Simulate and audit closed and open quantum-system models with QuTiP 5, including deterministic, trajectory, steady-state, spectral, and phase-space workflows. Use for local quantum-dynamics work where physical assumptions, dimensions, and numerical convergence must be explicit.
license	MIT
compatibility	Requires Python 3.11+, uv, and qutip==5.3.0 for executable simulations. Bundled planners and all script help run with the Python standard library; plotting requires the pinned graphics extra. No network service or credentials are used.
metadata	{"version":"1.1","skill-author":"K-Dense Inc.","last-reviewed":"2026-07-23"}

QuTiP 5

Scope

Use QuTiP for finite-dimensional quantum mechanics, quantum optics, Lindblad dynamics, trajectories, weak-coupling Bloch-Redfield models, and specialized Floquet, HEOM, and permutational-invariance methods. It is not a hardware execution SDK. Circuit and control functionality moved to separate QuTiP family packages.

This skill targets QuTiP 5.3.0, released 2026-05-22. QuTiP 5.3 requires Python 3.11 or newer. Its required distributions are NumPy (>=1.23.2), SciPy (>=1.9.2, excluding 1.16.0 and 1.17.0), and packaging.

Reproducible uv snapshot

Create a dedicated environment and pin every direct distribution:

uv venv --python 3.11
uv pip install "qutip==5.3.0"

For plots:

uv pip install "qutip[graphics]==5.3.0"

Optional QuTiP family packages are independently versioned:

uv pip install "qutip-qip==0.4.2"
uv pip install "qutip-qtrl==0.2.0"
uv pip install "qutip-jax==0.1.1"

• qutip-qip 0.4.2 (2026-06-23) is the production/stable circuit, gate, and noisy-device simulation package. Import from qutip_qip, not qutip.qip.
• qutip-qtrl 0.2.0 (2026-06-23) provides GRAPE and CRAB quantum optimal control. It is not a trajectory viewer. Import from qutip_qtrl, not qutip.control; PyPI still classifies it pre-alpha.
• qutip-jax 0.1.1 (2025-05-29) is the official JAX data backend for GPU and automatic-differentiation experiments. It is explicitly pre-alpha.
• qutip-cupy is an official QuTiP-organization repository, but it has no PyPI release and its own README says it is not officially released. Do not put an unreleased Git install into a reproducible workflow.

Use a project lockfile or a hash-generating uv pip compile workflow when transitive dependency identity must also be frozen.

Non-negotiable model contract

Before solving, record:

1. Units and convention. QuTiP equations normally set (\hbar=1). Hamiltonian entries are angular frequencies and rates have reciprocal-time units. Convert cyclic frequency with (2\pi f); never mix Hz and rad/s.
2. Subsystem order. tensor(A, B, C) fixes subsystem indices 0, 1, 2. Preserve that order in every state, operator, collapse channel, and partial trace. obj.ptrace([0, 2]) keeps those subsystems; it does not trace them.
3. State validity. Check ket norm or density-matrix Hermiticity, unit trace, and eigenvalues above a stated negative tolerance. Tiny negative values may be numerical; material negativity invalidates a claimed state.
4. Generator meaning. A Lindblad channel with rate gamma is represented by sqrt(gamma) * A, not gamma * A. Define what each rate measures. For example, sqrt(gamma_phi / 2) * sigmaz() gives coherence decay exp(-gamma_phi * t).
5. Approximations. State rotating-wave, Born-Markov, secular, weak-coupling, bath-equilibrium, truncation, symmetry, and initial-factorization assumptions wherever used.
6. Numerics. Justify Hilbert truncation, output grid, integration method, tolerances, trajectory count, and random seeds. Report result.stats.
7. Convergence. Sweep every artificial cutoff: Fock dimension, time/frequency window and spacing, ODE tolerances, trajectories, Floquet harmonics, HEOM depth and bath exponents, or PIQS representation as applicable.

Qobj, dimensions, and tensor order

Prefer explicit imports and inspect both shape and structured dimensions:

from qutip import basis, qeye, sigmaz, tensor

psi = tensor(basis(2, 0), basis(3, 1))
z_on_first = tensor(sigmaz(), qeye(3))

assert psi.shape == (6, 1)
assert psi.dims == [[2, 3], [1]]
assert z_on_first.dims == [[2, 3], [2, 3]]
rho_first = psi.proj().ptrace(0)  # keep subsystem 0

Matrix shape alone is insufficient: two objects can both be 6-by-6 but encode different tensor factorizations. Read references/core_concepts.md before building composite, superoperator, or channel models.

Choose the solver by physics

Model	Current API	Required justification
Closed, pure, unitary	sesolve	Hermitian Hamiltonian; no dissipation
Lindblad/open or mixed	mesolve	Markovian completely positive model and channel rates
Quantum jumps	mcsolve	Unravelling, trajectory convergence, seeds
Microscopic weak bath	brmesolve	Born-Markov/weak coupling, spectra, secular choice
Diffusive measurement	ssesolve, smesolve	monitored versus unmonitored channels
Periodic drive	FloquetBasis, fsesolve, fmmesolve	verified period and Floquet convergence
Structured non-Markovian bath	qutip.solver.heom	bath expansion and hierarchy convergence
Symmetric spin ensemble	qutip.piqs	permutation symmetry and basis choice

Do not select a more specialized solver merely because it exists.

Deterministic open-system example

QuTiP 5.3 uses ordinary option dictionaries. Solver controls, e_ops, and args are keyword-only; the old mutable options object is gone.

import numpy as np
from qutip import basis, mesolve, sigmam, sigmaz

omega = 2.0
gamma = 0.15
tlist = np.linspace(0.0, 20.0, 401)
excited = basis(2, 0)

result = mesolve(
    0.5 * omega * sigmaz(),
    excited,
    tlist,
    c_ops=[np.sqrt(gamma) * sigmam()],
    e_ops={"sigma_z": sigmaz(), "excited": excited.proj()},
    options={
        "method": "adams",
        "atol": 1e-10,
        "rtol": 1e-8,
        "store_final_state": True,
        "progress_bar": "",
    },
)

population = np.asarray(result.e_data["excited"])
assert np.max(np.abs(population - np.exp(-gamma * tlist))) < 2e-6
assert isinstance(result.stats, dict)

If the problem is stiff, compare bdf or lsoda; do not change an integrator without rerunning tolerance and invariant checks. QuTiP 5.3 also supports options={"matrix_form": True} in mesolve; benchmark and validate it before using it as a default.

Time-dependent systems

Prefer trusted Pythonic callables or numeric coefficient arrays. Do not create coefficient source strings from user input.

import numpy as np
from qutip import QobjEvo, sigmax, sigmaz

def envelope(t, amplitude, center, width):
    return amplitude * np.exp(-0.5 * ((t - center) / width) ** 2)

H = QobjEvo(
    [0.5 * sigmaz(), [sigmax(), envelope]],
    args={"amplitude": 0.2, "center": 5.0, "width": 1.0},
)
instantaneous_H = H(5.0)
H.arguments(amplitude=0.1)

The older f(t, args) coefficient signature is deprecated in 5.3 and is scheduled for removal in 5.5. See references/time_evolution.md.

Trajectories and stochastic solvers

import numpy as np
from qutip import basis, mcsolve, sigmam, sigmaz

tlist = np.linspace(0.0, 10.0, 201)
result = mcsolve(
    0.5 * sigmaz(),
    basis(2, 0),
    tlist,
    [np.sqrt(0.2) * sigmam()],
    e_ops=[basis(2, 0).proj()],
    ntraj=400,
    seeds=20260723,
    options={"keep_runs_results": False, "progress_bar": ""},
)

Report ntraj, result.seeds, uncertainty or repeated-seed sensitivity, and whether individual runs were retained. Reuse seeds=previous_result.seeds only when paired trajectories are intentional. ssesolve and smesolve use the boolean heterodyne argument, not legacy integer noise codes.

Steady states, spectra, and phase space

import numpy as np
from qutip import QFunc, liouvillian, operator_to_vector, qfunc, steadystate

rho_ss = steadystate(H, c_ops, method="direct")
residual = (liouvillian(H, c_ops) * operator_to_vector(rho_ss)).norm()
assert residual < 1e-9

xvec = np.linspace(-5.0, 5.0, 151)
Q_once = qfunc(rho_ss, xvec, xvec)
q_many = QFunc(xvec, xvec)
Q_again = q_many(rho_ss)
assert Q_once.shape == (len(xvec), len(xvec))

For wigner, qfunc, and QFunc, array element [j, k] corresponds to yvec[j], xvec[k]. In QuTiP 5.3, QFunc is initialized with fixed coordinates and called with a state; it has no .eval method. This skill never uses Python dynamic-code execution. Prefer plot_wigner, Result.plot_expect, or explicit Matplotlib axes as documented in references/visualization.md.

Direct spectrum is a stationary steady-state spectrum. An FFT of a finite correlation requires explicit checks for tail decay, timestep aliasing, frequency resolution, window sensitivity, and transform convention. See references/analysis.md.

Advanced boundaries

• Import HEOM from qutip.solver.heom; the legacy QuTiP 4 nonmarkov HEOM namespace is stale.
• Use FloquetBasis for modes and quasi-energies. Verify H(t + T) == H(t) numerically and sweep basis/truncation choices.
• Access PIQS with from qutip import piqs. Dicke.pisolve is only the optimized diagonal-state/diagonal-Hamiltonian route; general Dicke-basis dynamics use the Liouvillian with mesolve.
• brmesolve can violate positivity, especially without secularization. Check density-matrix eigenvalues over time.
• QIP and optimal control are extension-package concerns. Never present local simulation as quantum-hardware execution.

See references/advanced.md for HEOM, Floquet, PIQS, stochastic, and extension boundaries.

Safe local CLIs

All bundled tools are local-only, emit strict JSON, reject non-finite JSON and unknown keys, and never load pickle files or executable model code. Simulation imports are lazy, so every --help works without QuTiP installed.

Script	Purpose
scripts/qobj_model_validator.py	Validate bounded Qobj model JSON, dimensions, states, rates, and role compatibility
scripts/two_level_simulation.py	Run a bounded two-level Lindblad or jump simulation
scripts/solver_config_planner.py	Select a current solver and option/checklist plan
scripts/convergence_sweep.py	Sweep tolerances/grid size or trajectory count on a synthetic model
scripts/result_audit.py	Audit JSON output without deserializing Python objects
scripts/steady_state_spectrum_planner.py	Plan bounded steady-state and direct/FFT spectral checks

Example:

python skills/qutip/scripts/two_level_simulation.py --help
python skills/qutip/scripts/two_level_simulation.py \
  --decay-rate 0.2 --t-final 10 --time-points 201 \
  --output two-level.json
python skills/qutip/scripts/result_audit.py two-level.json

Completion checklist

• Record units, (\hbar), tensor order, initial state, channels, and model assumptions.
• Validate Hermiticity, norm/trace, positivity, dimensions, and generator units.
• Pin QuTiP and direct extensions; record platform, Python, NumPy, and SciPy.
• Inspect result options and stats; do not assume states were stored.
• Perform cutoff, grid, tolerance/integrator, and stochastic convergence sweeps.
• Save portable numeric/configuration summaries as JSON or text. Do not load untrusted QuTiP object/result files because object serialization can execute code.

References

• references/core_concepts.md — Qobj, dimensions, tensor products, states, channels, and unit conventions
• references/time_evolution.md — current solver signatures, options, results, QobjEvo, trajectories, and numerical controls
• references/analysis.md — physical-state audits, steady states, correlations, spectra, and convergence
• references/visualization.md — Wigner, Q functions, QFunc, Bloch, result, and matrix plots
• references/advanced.md — Bloch-Redfield, stochastic, Floquet, HEOM, PIQS, and QuTiP family package boundaries

Dated official sources

Verified 2026-07-23:

• QuTiP 5.3.0 PyPI metadata
• QuTiP 5.3.0 release
• QuTiP 5.3 changelog
• QuTiP 5.3 API
• QuTiP version-5 tutorials
• qutip-qip PyPI
• qutip-qtrl PyPI
• qutip-jax PyPI
• official unreleased qutip-cupy repository