qutip

name	qutip
version	1.1.1
description	Simulates quantum states and open systems in QuTiP 5.x (sesolve, mesolve, mcsolve, entanglement, Bloch/Wigner, Floquet, HEOM). Use when evolving Qobj states, measuring fidelity or entropy, or visualizing Bloch/Wigner plots. Not for classical ODEs, large-scale circuit transpilation (qiskit/cirq), or loading untrusted .qu pickles.
risk	safe
source	openrouter-deepsearch
date_added	2026-06-16T00:00:00.000Z

QuTiP: Quantum Toolbox in Python

When to Use

Reach for QuTiP whenever the object you care about is a quantum state or operator on a finite-dimensional Hilbert space and you need to evolve it, measure it, or visualize it. The library represents Qobj (quantum objects) together with their tensor-product dimension structure, so composite systems, partial traces, and superoperators all stay bookkeeping-correct without you tracking index order by hand.

Concretely, it fits when you are:

1. Modeling closed (unitary) or open (dissipative) systems. Closed systems conserve probability and you only need the Hamiltonian; open systems leak into an environment, so you also supply collapse operators. QuTiP lets you switch between the two by changing a single argument.
2. Working with states and operators directly. Kets, bras, and density matrices share one Qobj type, so the same code path handles pure and mixed states — important because real experiments almost always produce mixed states.
3. Solving the Schrödinger or master equation for dynamics. You rarely have closed-form solutions for driven or dissipative systems, so a numerical integrator is the practical tool.
4. Quantifying quantum information (entanglement, fidelity, von Neumann entropy). These scalar diagnostics let you compare a noisy simulation against an ideal target.
5. Visualizing states via Bloch spheres, Wigner functions, or Hinton diagrams, because a phase-phase or sphere picture often reveals structure (squeezing, interference fringes) that a raw density matrix hides.
6. Running advanced dynamics such as Floquet theory (periodic driving) or HEOM (strong, non-Markovian system–bath coupling) where the simple Born–Markov master equation is no longer valid.

Do Not Use

• Classical physics simulations. QuTiP has no advantage over plain NumPy/SciPy for classical ODEs, and forcing classical problems through Qobj only adds overhead.
• Large-scale circuit synthesis / transpilation. Use the qutip-qip extension for small pedagogical circuits, but for hardware-targeted compilation prefer qiskit or cirq. The legacy qutip-qip namespace is deprecated; new code should import from qutip_qip.
• Hilbert spaces that exceed available RAM. A dense density matrix costs O(d^2) complex numbers, so a 10-qubit open system (d = 1024) already needs ~16 MB per stored matrix and far more transiently. Before scaling up, switch to sparse representations, Krylov-subspace methods (options={"method": "krylov"}), Monte-Carlo trajectories (state vectors instead of density matrices), or a GPU back-end.
• Loading untrusted serialized objects. Qobj pickles and .qu files can carry arbitrary Python objects. Deserializing attacker-controlled data is equivalent to running attacker code. Only load files whose provenance you trust, and prefer interchange formats (.npy, HDF5 with array-only payloads) for data from third parties.

Prerequisites

Installation

Pin to the 5.x series. QuTiP 5 changed several public APIs (solver options are now plain dictionaries, Floquet uses FloquetBasis, HEOM moved to qutip.solver.heom), so pinning a major version protects your scripts from silent breakage on the next release:

uv pip install "qutip>=5.0,<6.0"

Optional companion packages — install only what a given project needs, since each pulls in extra dependencies:

# Quantum information processing (circuits, gates) — modern namespace is `qutip_qip`
uv pip install "qutip-qip>=0.4"

# Optimal-control / pulse-engineering tools
uv pip install "qutip-qtrl>=0.3"

Verify the install and surface the exact version, because feature availability (Floquet, HEOM, solver option keys) differs across point releases:

import qutip

print(f"QuTiP {qutip.__version__}")
# Run the built-in self-test on a fresh environment to catch a broken BLAS/SciPy link
# before it shows up as a wrong numerical answer.
# qutip.about()  # prints versions of NumPy, SciPy, Cython, and compiled extensions

Coding Conventions

Every code block below is a complete, runnable snippet that follows three rules. Each rule prevents a specific, common failure mode:

1. Explicit imports and explicit type hints (no Any). from qutip import * pulls in names like num, create, and position that shadow ordinary identifiers and make it hard to tell where a symbol came from. Naming imports and annotating with concrete types (Qobj, NDArray[np.float64], list[Qobj]) lets a type checker catch a ket/operator mix-up before you wait through a long integration.
2. Validation at the boundary. Numerical solvers do not reject unphysical input — a negative decay rate or mismatched Hilbert-space dimension produces a cryptic error deep in the integrator, or worse, a plausible-looking but wrong answer. Validating dimensions and rates where data enters the function converts those into clear, early errors.
3. Defensive handling of solver output. An ODE integrator that diverges returns NaN or inf rather than raising. Checking that results are finite (and, for steady states, that the residual is actually small) is the difference between trusting a number and being misled by one.

The examples share a small helper module. Save it once as qutip_helpers.py:

# qutip_helpers.py
"""Reusable validation helpers for QuTiP simulations (Python >= 3.11)."""
from __future__ import annotations

import numpy as np
from numpy.typing import NDArray
from qutip import Qobj


def require_dimension(name: str, value: int, *, minimum: int = 1) -> int:
    """Validate a Hilbert-space dimension.

    A solver given a non-positive or non-integer dimension fails with an opaque
    shape error; checking here names the offending argument instead.
    """
    if not isinstance(value, int) or value < minimum:
        raise ValueError(f"{name} must be an int >= {minimum}, got {value!r}")
    return value


def require_nonnegative_rate(name: str, value: float) -> float:
    """Validate a physical rate (decay, dephasing, coupling magnitude).

    Negative or non-finite rates correspond to an unphysical, norm-amplifying
    master equation; the integrator would diverge silently.
    """
    rate = float(value)
    if not np.isfinite(rate) or rate < 0.0:
        raise ValueError(f"{name} must be a finite, non-negative rate, got {value!r}")
    return rate


def require_time_grid(tlist: NDArray[np.float64]) -> NDArray[np.float64]:
    """Validate the integration time grid.

    QuTiP samples results at these points; a non-monotonic or single-point grid
    yields meaningless output rather than an error.
    """
    grid = np.asarray(tlist, dtype=np.float64)
    if grid.ndim != 1 or grid.size < 2:
        raise ValueError("tlist must be a 1-D array with at least two time points")
    if not np.all(np.diff(grid) > 0.0):
        raise ValueError("tlist must be strictly increasing")
    return grid


def require_hermitian(name: str, operator: Qobj) -> Qobj:
    """Validate that an operator is Hermitian.

    A non-Hermitian Hamiltonian breaks unitarity and the conservation of
    probability. (Relax this guard only for deliberately non-Hermitian effective
    Hamiltonians, e.g. PT-symmetric models.)
    """
    if not operator.isherm:
        raise ValueError(f"{name} must be Hermitian for standard time evolution")
    return operator


def require_density_matrix(name: str, rho: Qobj, *, atol: float = 1e-6) -> Qobj:
    """Validate that `rho` is a unit-trace density operator.

    Information measures (entropy, concurrence) are only meaningful for a valid
    normalized state; a trace far from 1 signals an upstream bug.
    """
    if not rho.isoper:
        raise TypeError(f"{name} must be a density operator (got type={rho.type!r})")
    trace = complex(rho.tr())
    if abs(trace - 1.0) > atol:
        raise ValueError(f"{name} trace must equal 1, got {trace.real:.6f}")
    return rho

Procedure

1. Quantum Objects and States

States and operators are both Qobj instances; the difference is encoded in the .type attribute and the .dims structure. Building them through small validated constructors keeps the truncation dimension explicit, which matters because every infinite-dimensional mode (a cavity, an oscillator) must be cut off somewhere, and choosing that cutoff too low silently corrupts the physics.

from qutip import (
    Qobj,
    basis,
    coherent,
    thermal_dm,
    destroy,
    create,
    num,
    qeye,
    sigmax,
    sigmay,
    sigmaz,
    tensor,
)

from qutip_helpers import require_dimension, require_nonnegative_rate


def fock_state(dimension: int, level: int) -> Qobj:
    """Return the Fock (number) state |level> in a `dimension`-sized space."""
    require_dimension("dimension", dimension)
    if not isinstance(level, int) or not (0 <= level < dimension):
        raise ValueError(
            f"level must satisfy 0 <= level < {dimension}, got {level!r}"
        )
    return basis(dimension, level)


def coherent_state(dimension: int, alpha: complex) -> Qobj:
    """Return a coherent state |alpha> with a truncation safety check."""
    require_dimension("dimension", dimension)
    amplitude = complex(alpha)
    mean_photons = abs(amplitude) ** 2
    # A coherent state populates Fock levels up to ~|alpha|^2 with a spread of
    # ~|alpha|. If the truncation is too small the tail wraps around and aliases
    # back into low levels, so refuse rather than return a corrupted state.
    if mean_photons > 0.5 * dimension:
        raise ValueError(
            f"dimension={dimension} too small for <n>={mean_photons:.1f}; "
            f"use dimension >= {int(4 * mean_photons) + 1} to contain the tail"
        )
    return coherent(dimension, amplitude)


def thermal_state(dimension: int, mean_photons: float) -> Qobj:
    """Return a thermal density matrix with the given average occupation."""
    require_dimension("dimension", dimension)
    n_avg = require_nonnegative_rate("mean_photons", mean_photons)
    if n_avg > 0.25 * dimension:
        raise ValueError(
            f"dimension={dimension} too small for a thermal state with "
            f"<n>={n_avg:.1f}; increase the truncation"
        )
    return thermal_dm(dimension, n_avg)


def mode_operators(dimension: int) -> tuple[Qobj, Qobj, Qobj]:
    """Return (annihilation, creation, number) operators for one bosonic mode."""
    require_dimension("dimension", dimension)
    annihilation = destroy(dimension)
    creation = create(dimension)
    number = num(dimension)
    return annihilation, creation, number


def two_qubit_observable() -> Qobj:
    """Build sigma_z ⊗ I on a two-qubit space, demonstrating composition.

    `tensor` tracks the [[2, 2], [2, 2]] dims so later partial traces and
    expectation values address the correct subsystem automatically.
    """
    sz_on_first = tensor(sigmaz(), qeye(2))
    return sz_on_first


# Example usage
psi_fock = fock_state(dimension=20, level=2)            # |2>
psi_coherent = coherent_state(dimension=20, alpha=2.0)  # |alpha=2>
rho_thermal = thermal_state(dimension=20, mean_photons=1.5)
a_op, adag_op, n_op = mode_operators(20)
sx, sy, sz = sigmax(), sigmay(), sigmaz()

Load references/core_concepts.md when you need comprehensive coverage of quantum objects, states, operators, tensor products, partial traces, or superoperator formalism.

2. Time Evolution and Dynamics

Pick the solver that matches the physics, not out of habit — the wrong choice is either needlessly slow or silently inapplicable. The wrapper below validates the model, supplies sane solver options as a QuTiP 5 dictionary, and verifies the output is finite so a diverged integration cannot masquerade as a result.

from collections.abc import Sequence

import numpy as np
from numpy.typing import NDArray
from qutip import Qobj, Result, mesolve, mcsolve, sesolve

from qutip_helpers import require_hermitian, require_time_grid, require_nonnegative_rate


def evolve_closed_system(
    hamiltonian: Qobj,
    initial_state: Qobj,
    tlist: NDArray[np.float64],
    expect_ops: Sequence[Qobj],
    *,
    options: dict[str, object] | None = None,
) -> Result:
    """Unitary evolution via the Schrödinger equation (fastest, pure states)."""
    grid = require_time_grid(tlist)
    require_hermitian("hamiltonian", hamiltonian)
    if not initial_state.isket:
        raise TypeError("sesolve expects a ket initial state; use mesolve for mixed states")

    solver_options: dict[str, object] = {"atol": 1e-8, "rtol": 1e-6, "nsteps": 10_000}
    if options:
        solver_options.update(options)
    try:
        result = sesolve(
            hamiltonian, initial_state, grid,
            e_ops=list(expect_ops), options=solver_options,
        )
    except Exception as exc:  # surface integrator failure with context
        raise RuntimeError("sesolve failed to integrate the Schrödinger equation") from exc
    _assert_finite_expectations(result)
    return result


def evolve_open_system(
    hamiltonian: Qobj,
    initial_state: Qobj,
    tlist: NDArray[np.float64],
    collapse_ops: Sequence[Qobj],
    expect_ops: Sequence[Qobj],
    *,
    options: dict[str, object] | None = None,
) -> Result:
    """Dissipative evolution via the Lindblad master equation (mixed states)."""
    grid = require_time_grid(tlist)
    require_hermitian("hamiltonian", hamiltonian)
    # Mismatched dims are the most common open-systems bug; check before the
    # integrator turns them into an inscrutable broadcasting error.
    for index, c_op in enumerate(collapse_ops):
        if c_op.dims != hamiltonian.dims:
            raise ValueError(
                f"collapse_ops[{index}] dims {c_op.dims} != H dims {hamiltonian.dims}"
            )

    solver_options: dict[str, object] = {
        "atol": 1e-8,
        "rtol": 1e-6,
        "nsteps": 10_000,
        "store_states": False,  # keep only e_ops unless states are explicitly needed
    }
    if options:
        solver_options.update(options)
    try:
        result = mesolve(
            hamiltonian, initial_state, grid,
            c_ops=list(collapse_ops), e_ops=list(expect_ops),
            options=solver_options,
        )
    except Exception as exc:
        raise RuntimeError("mesolve failed to integrate the master equation") from exc
    _assert_finite_expectations(result)
    return result


def evolve_trajectories(
    hamiltonian: Qobj,
    initial_state: Qobj,
    tlist: NDArray[np.float64],
    collapse_ops: Sequence[Qobj],
    expect_ops: Sequence[Qobj],
    *,
    ntraj: int = 500,
    options: dict[str, object] | None = None,
) -> Result:
    """Quantum-jump (Monte Carlo) unravelling; cheaper memory than mesolve at large d."""
    if ntraj < 1:
        raise ValueError(f"ntraj must be >= 1, got {ntraj}")
    grid = require_time_grid(tlist)
    require_hermitian("hamiltonian", hamiltonian)

    solver_options: dict[str, object] = {"atol": 1e-8, "rtol": 1e-6, "map": "parallel"}
    if options:
        solver_options.update(options)
    try:
        result = mcsolve(
            hamiltonian, initial_state, grid,
            c_ops=list(collapse_ops), e_ops=list(expect_ops),
            ntraj=ntraj, options=solver_options,
        )
    except Exception as exc:
        raise RuntimeError("mcsolve failed to run the trajectory ensemble") from exc
    _assert_finite_expectations(result)
    return result


def _assert_finite_expectations(result: Result) -> None:
    """Reject diverged integrations: a runaway ODE returns NaN/inf, not an error."""
    for index, series in enumerate(result.expect):
        values = np.asarray(series)
        if not np.all(np.isfinite(values)):
            raise RuntimeError(
                f"expectation series {index} is non-finite; the solver likely diverged. "
                "Reduce the time step, lower the rates, or set options['method']='bdf'."
            )

Solver selection guide — and why each one exists:

Solver	Use case	Cost
sesolve	Pure states under unitary evolution. Propagates a state vector (d amplitudes).	Cheapest
mesolve	Mixed states and general dissipation. Propagates a density matrix (d^2 numbers).	Exact ensemble-averaged dynamics
mcsolve	Stochastic quantum jumps. Averages many d-dimensional trajectories instead of storing one d^2 matrix.	Wins on memory for large open systems; exposes individual quantum-jump records
brmesolve	Bloch–Redfield, for weak system–bath coupling specified through a noise spectrum rather than explicit collapse operators.	Use when you know the bath's spectral density but not a phenomenological decay rate
fmmesolve	Floquet–Markov, for time-periodic driving where dissipation is best treated in the Floquet basis.	For periodically driven open systems

Load references/time_evolution.md when you need time-dependent Hamiltonians, propagators, or the full solver option set.

3. Analysis and Measurement

These functions turn a state into a scalar you can track or compare. Each guard reflects the domain of validity: entropy and concurrence require a normalized density matrix, fidelity requires two states on the same space, and a steady state is only trustworthy if its Liouvillian residual is actually near zero.

import numpy as np
from numpy.typing import NDArray
from qutip import (
    Qobj,
    concurrence,
    correlation_2op_1t,
    entropy_vn,
    expect,
    fidelity,
    liouvillian,
    operator_to_vector,
    spectrum_correlation_fft,
    steadystate,
    tracedist,
)

from qutip_helpers import require_density_matrix


def von_neumann_entropy(rho: Qobj) -> float:
    """S(rho) = -Tr(rho ln rho), in nats. 0 for a pure state, ln(d) when maximally mixed."""
    require_density_matrix("rho", rho)
    return float(entropy_vn(rho))


def two_qubit_concurrence(rho: Qobj) -> float:
    """Wootters concurrence in [0, 1]; 0 = separable, 1 = maximally entangled."""
    require_density_matrix("rho", rho)
    if rho.dims != [[2, 2], [2, 2]]:
        raise ValueError(f"concurrence is defined for two qubits, got dims {rho.dims}")
    return float(concurrence(rho))


def state_fidelity(state_a: Qobj, state_b: Qobj) -> float:
    """Jozsa fidelity in [0, 1]; accepts kets or density matrices on the same space."""
    if state_a.dims[0] != state_b.dims[0]:
        raise ValueError(
            f"states live on different spaces: {state_a.dims[0]} vs {state_b.dims[0]}"
        )
    return float(fidelity(state_a, state_b))


def trace_distance(rho_a: Qobj, rho_b: Qobj) -> float:
    """Trace distance in [0, 1]; the maximal single-shot distinguishability of two states."""
    require_density_matrix("rho_a", rho_a)
    require_density_matrix("rho_b", rho_b)
    return float(tracedist(rho_a, rho_b))


def expectation_value(operator: Qobj, state: Qobj) -> float | complex:
    """Compute <operator> for a ket or density matrix."""
    return expect(operator, state)


def stationary_state(
    hamiltonian: Qobj,
    collapse_ops: list[Qobj],
    *,
    tol: float = 1e-10,
) -> Qobj:
    """Solve for the steady state of a Liouvillian and verify the residual is small."""
    L = liouvillian(hamiltonian, collapse_ops)
    try:
        rho_ss = steadystate(L, method="svd", tol=tol)
    except Exception as exc:
        raise RuntimeError("steadystate failed to converge") from exc
    # Verify: L(rho_ss) should be ~0. If it is not, the solver returned garbage.
    residual = (L * operator_to_vector(rho_ss)).norm()
    if residual > 1e-6:
        raise RuntimeError(
            f"steady-state residual {residual:.2e} exceeds tolerance; "
            "the Liouvillian may be singular or the system may not have a unique steady state"
        )
    return rho_ss


def power_spectrum(
    hamiltonian: Qobj,
    collapse_ops: list[Qobj],
    operator_a: Qobj,
    *,
    tlist: NDArray[np.float64],
) -> NDArray[np.float64]:
    """Compute the emission spectrum S(omega) via the two-time correlation function."""
    corr = correlation_2op_1t(
        hamiltonian, None, tlist, collapse_ops, operator_a.dag(), operator_a,
    )
    spectrum, frequencies = spectrum_correlation_fft(tlist, corr)
    return np.asarray(spectrum, dtype=np.float64)

Load references/analysis.md when you need detailed coverage of correlation functions, spectrum computation, or advanced steady-state methods.

4. Visualization

from __future__ import annotations

import numpy as np
from numpy.typing import NDArray
from qutip import Qobj, Bloch, wigner, plot_wigner, plot_fock_distribution


def render_bloch(
    states: list[Qobj],
    *,
    save_path: str | None = None,
    figsize: tuple[int, int] = (5, 5),
) -> None:
    """Plot one or more single-qubit states on a Bloch sphere.

    Works headless: if `save_path` is given, the figure is written to disk
    without calling `plt.show()`.
    """
    import matplotlib.pyplot as plt

    bloch = Bloch(figsize=figsize)
    for state in states:
        if not state.isket or state.dims != [[2], [1]]:
            raise ValueError(
                f"render_bloch expects single-qubit kets, got dims={state.dims}, type={state.type}"
            )
        bloch.add_states(state)
    bloch.render()

    if save_path is not None:
        fig = plt.gcf()
        fig.savefig(save_path, dpi=150, bbox_inches="tight")
        plt.close(fig)
    else:
        plt.show()


def render_wigner(
    state: Qobj,
    *,
    save_path: str | None = None,
    xvec: NDArray[np.float64] | None = None,
) -> None:
    """Plot the Wigner function of a single-mode state."""
    import matplotlib.pyplot as plt

    if xvec is None:
        xvec = np.linspace(-5, 5, 200)
    wigner_values = wigner(state, xvec, xvec)

    fig, ax = plt.subplots(figsize=(6, 5))
    contour = ax.contourf(xvec, xvec, wigner_values, 100, cmap="RdBu_r")
    ax.set_xlabel(r"Re($\alpha$)")
    ax.set_ylabel(r"Im($\alpha$)")
    fig.colorbar(contour, ax=ax)
    fig.tight_layout()

    if save_path is not None:
        fig.savefig(save_path, dpi=150, bbox_inches="tight")
        plt.close(fig)
    else:
        plt.show()

Load references/visualization.md when you need advanced plotting (Hinton diagrams, Wigner animation, sphere customization, or multi-panel figures).

5. Damped Harmonic Oscillator (Complete Example)

A cavity mode with photon loss: the coherent state amplitude decays as alpha(t) = alpha(0) * exp(-kappa*t/2), so the mean photon number follows |alpha|^2 * exp(-kappa*t).

"""Damped harmonic oscillator: coherent state losing photons to a cold bath."""
from __future__ import annotations

import numpy as np
from numpy.typing import NDArray
from qutip import Qobj, coherent, destroy, mesolve, num, qeye

from qutip_helpers import (
    require_dimension,
    require_nonnegative_rate,
    require_time_grid,
    require_hermitian,
)


def run_damped_oscillator(
    dimension: int,
    frequency: float,
    decay_rate: float,
    initial_alpha: complex,
    tlist: NDArray[np.float64],
) -> NDArray[np.float64]:
    """Return <n>(t) for a damped cavity initially in a coherent state."""
    require_dimension("dimension", dimension)
    kappa = require_nonnegative_rate("decay_rate", decay_rate)
    grid = require_time_grid(tlist)

    a = destroy(dimension)
    hamiltonian = frequency * a.dag() * a
    require_hermitian("hamiltonian", hamiltonian)
    collapse_ops = [np.sqrt(kappa) * a]
    initial_state = coherent(dimension, initial_alpha)

    # Truncation safety: coherent state tail must fit in the Fock space
    mean_photons = abs(complex(initial_alpha)) ** 2
    if mean_photons > 0.5 * dimension:
        raise ValueError(
            f"dimension={dimension} too small for |alpha|={abs(complex(initial_alpha)):.1f} "
            f"(<n>={mean_photons:.1f}); use dimension >= {int(4 * mean_photons) + 1}"
        )

    try:
        result = mesolve(
            hamiltonian, initial_state, grid,
            c_ops=collapse_ops, e_ops=[num(dimension)],
            options={"atol": 1e-8, "rtol": 1e-6, "nsteps": 10_000},
        )
    except Exception as exc:
        raise RuntimeError("mesolve failed for the damped oscillator") from exc

    photon_number = np.asarray(result.expect[0], dtype=np.float64)
    if not np.all(np.isfinite(photon_number)):
        raise RuntimeError("non-finite photon number; the integration diverged")
    return photon_number


def main() -> None:
    import matplotlib.pyplot as plt

    dimension = 20
    decay_rate = 0.1
    tlist = np.linspace(0.0, 50.0, 200)
    photon_number = run_damped_oscillator(
        dimension=dimension, frequency=1.0, decay_rate=decay_rate,
        initial_alpha=3.0, tlist=tlist,
    )

    expected = (abs(3.0) ** 2) * np.exp(-decay_rate * tlist)  # analytic envelope
    figure, ax = plt.subplots(figsize=(8, 4))
    ax.plot(tlist, photon_number, label="mesolve")
    ax.plot(tlist, expected, "--", label=r"$|\alpha|^2 e^{-\kappa t}$")
    ax.set_xlabel("Time")
    ax.set_ylabel(r"$\langle n \rangle$")
    ax.set_title("Photon-number decay")
    ax.legend()
    figure.tight_layout()
    plt.show()


if __name__ == "__main__":
    main()

6. Two-Qubit Entanglement Dynamics (Complete Example)

Pure dephasing destroys the off-diagonal coherences of a Bell state without changing populations, so the concurrence should decay smoothly to zero. The Hamiltonian is set to zero deliberately, to isolate the effect of dephasing from any coherent rotation.

"""Concurrence decay of a Bell state under independent local dephasing."""
from __future__ import annotations

import numpy as np
from numpy.typing import NDArray
from qutip import Qobj, bell_state, mesolve, qeye, sigmaz, tensor

from qutip_helpers import require_nonnegative_rate, require_time_grid, require_density_matrix
from qutip import concurrence


def run_dephasing(
    dephasing_rate: float,
    tlist: NDArray[np.float64],
) -> NDArray[np.float64]:
    """Return concurrence C(t) for |Phi+> under sigma_z dephasing on each qubit."""
    gamma = require_nonnegative_rate("dephasing_rate", dephasing_rate)
    grid = require_time_grid(tlist)

    initial_state: Qobj = bell_state("00")  # (|00> + |11>)/sqrt(2)
    # Zero Hamiltonian: no coherent dynamics, so we observe dephasing in isolation.
    hamiltonian: Qobj = 0.0 * tensor(qeye(2), qeye(2))
    collapse_ops: list[Qobj] = [
        np.sqrt(gamma) * tensor(sigmaz(), qeye(2)),
        np.sqrt(gamma) * tensor(qeye(2), sigmaz()),
    ]

    try:
        result = mesolve(
            hamiltonian, initial_state, grid,
            c_ops=collapse_ops, e_ops=[], options={"store_states": True},
        )
    except Exception as exc:
        raise RuntimeError("mesolve failed for the dephasing model") from exc

    # With collapse operators present, result.states are already density matrices,
    # so concurrence is computed on them directly (no .proj() needed).
    concurrences: list[float] = []
    for state in result.states:
        require_density_matrix("state", state, atol=1e-4)
        concurrences.append(float(concurrence(state)))
    return np.asarray(concurrences, dtype=np.float64)


def main() -> None:
    import matplotlib.pyplot as plt

    tlist = np.linspace(0.0, 10.0, 100)
    concurrences = run_dephasing(dephasing_rate=0.1, tlist=tlist)

    if concurrences[-1] >= concurrences[0]:
        raise RuntimeError("entanglement did not decay under dephasing — model is wrong")

    figure, ax = plt.subplots(figsize=(8, 4))
    ax.plot(tlist, concurrences)
    ax.set_xlabel("Time")
    ax.set_ylabel("Concurrence")
    ax.set_title("Entanglement decay under local dephasing")
    ax.set_ylim(0.0, 1.05)
    figure.tight_layout()
    plt.show()


if __name__ == "__main__":
    main()

7. Jaynes–Cummings Model (Complete Example)

A cavity field coupled to a two-level atom exchanges energy coherently (vacuum Rabi oscillations) while both subsystems leak to the environment. This example exercises tensor products, multiple collapse channels, and per-subsystem observables at once.

"""Jaynes–Cummings model: cavity and atom occupation under joint dissipation."""
from __future__ import annotations

import numpy as np
from numpy.typing import NDArray
from qutip import (
    Qobj,
    basis,
    coherent,
    destroy,
    mesolve,
    qeye,
    sigmam,
    tensor,
)

from qutip_helpers import (
    require_dimension,
    require_nonnegative_rate,
    require_time_grid,
    require_hermitian,
)


def build_jaynes_cummings(
    cavity_dim: int,
    cavity_freq: float,
    atom_freq: float,
    coupling: float,
) -> tuple[Qobj, Qobj, Qobj]:
    """Return (H, cavity number op, atom number op) in the rotating-wave approximation."""
    require_dimension("cavity_dim", cavity_dim)
    wc = require_nonnegative_rate("cavity_freq", cavity_freq)
    wa = require_nonnegative_rate("atom_freq", atom_freq)
    g = require_nonnegative_rate("coupling", coupling)

    cavity = tensor(destroy(cavity_dim), qeye(2))
    atom = tensor(qeye(cavity_dim), sigmam())
    hamiltonian: Qobj = (
        wc * cavity.dag() * cavity
        + wa * atom.dag() * atom
        + g * (cavity.dag() * atom + cavity * atom.dag())
    )
    require_hermitian("hamiltonian", hamiltonian)
    return hamiltonian, cavity.dag() * cavity, atom.dag() * atom


def run_jaynes_cummings(
    cavity_dim: int,
    tlist: NDArray[np.float64],
    *,
    cavity_decay: float,
    atom_decay: float,
) -> tuple[NDArray[np.float64], NDArray[np.float64]]:
    """Return (<n_cavity>(t), <n_atom>(t)) for a coherent field and a ground-state atom."""
    grid = require_time_grid(tlist)
    kappa = require_nonnegative_rate("cavity_decay", cavity_decay)
    gamma = require_nonnegative_rate("atom_decay", atom_decay)

    hamiltonian, n_cavity, n_atom = build_jaynes_cummings(
        cavity_dim=cavity_dim, cavity_freq=1.0, atom_freq=1.0, coupling=0.05
    )
    cavity = tensor(destroy(cavity_dim), qeye(2))
    atom = tensor(qeye(cavity_dim), sigmam())
    collapse_ops: list[Qobj] = [np.sqrt(kappa) * cavity, np.sqrt(gamma) * atom]
    initial_state: Qobj = tensor(coherent(cavity_dim, 2.0), basis(2, 0))

    try:
        result = mesolve(
            hamiltonian, initial_state, grid,
            c_ops=collapse_ops, e_ops=[n_cavity, n_atom],
            options={"atol": 1e-8, "rtol": 1e-6, "nsteps": 10_000},
        )
    except Exception as exc:
        raise RuntimeError("mesolve failed for the Jaynes–Cummings model") from exc

    cavity_occupation = np.asarray(result.expect[0], dtype=np.float64)
    atom_occupation = np.asarray(result.expect[1], dtype=np.float64)
    if not (np.all(np.isfinite(cavity_occupation)) and np.all(np.isfinite(atom_occupation))):
        raise RuntimeError("non-finite occupations; the integration diverged")
    return cavity_occupation, atom_occupation


def main() -> None:
    import matplotlib.pyplot as plt

    tlist = np.linspace(0.0, 50.0, 200)
    cavity_occupation, atom_occupation = run_jaynes_cummings(
        cavity_dim=10, tlist=tlist, cavity_decay=0.1, atom_decay=0.05
    )

    figure, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
    axes[0].plot(tlist, cavity_occupation)
    axes[0].set_ylabel(r"$\langle n_\mathrm{cavity} \rangle$")
    axes[1].plot(tlist, atom_occupation)
    axes[1].set_ylabel(r"$\langle n_\mathrm{atom} \rangle$")
    axes[1].set_xlabel("Time")
    figure.tight_layout()
    plt.show()


if __name__ == "__main__":
    main()

Pitfalls

1. Truncation dimension too small for coherent/thermal states. A coherent state with |alpha|^2 mean photons populates Fock levels up to ~|alpha|^2 with a spread of ~|alpha. If the truncation is too small the tail wraps around and aliases back into low levels, silently corrupting the state. Always use dimension >= 4 * |alpha|^2 + 1 for coherent states and check that mean_photons <= 0.25 * dimension for thermal states.

2. Collapse operator dimensions mismatched with Hamiltonian. This is the most common open-systems bug. The integrator turns a dimension mismatch into an inscrutable broadcasting error. Always validate c_op.dims == hamiltonian.dims before calling mesolve.

3. Negative or non-finite rates passed to the master equation. These correspond to an unphysical, norm-amplifying master equation; the integrator diverges silently rather than raising. Always validate rates are finite and non-negative at the boundary.

4. Diverged integrator returning NaN/inf instead of raising. An ODE integrator that diverges returns NaN or inf rather than raising an exception. Always check np.all(np.isfinite(values)) on expectation series after solving. If divergence occurs, reduce the time step, lower the rates, or set options['method']='bdf'.

5. Non-Hermitian Hamiltonian for standard time evolution. A non-Hermitian Hamiltonian breaks unitarity and conservation of probability. Only relax the Hermiticity guard for deliberately non-Hermitian effective Hamiltonians (e.g. PT-symmetric models).

6. Steady state with large Liouvillian residual. steadystate may return a result even when the Liouvillian residual is not near zero. Always verify (L * operator_to_vector(rho_ss)).norm() < 1e-6 after solving. A large residual means the Liouvillian is singular or the system lacks a unique steady state.

7. Using from qutip import *. This pulls in names like num, create, and position that shadow ordinary identifiers. Use explicit imports instead.

8. Loading untrusted serialized Qobj pickles or .qu files. These can carry arbitrary Python objects. Deserializing attacker-controlled data is equivalent to running attacker code. Only load files whose provenance you trust; prefer or HDF5 with array-only payloads for third-party data.

Verification

1. Install and confirm version:

   uv pip install "qutip>=5.0,<6.0"
   python -c "import qutip; print(qutip.__version__)"
   

   Confirm a 5.x version is printed.

2. Save and import helpers:

   python -c "import qutip_helpers; print('helpers OK')"
   

3. Run the damped oscillator example and confirm <n>(t) tracks the analytic |alpha|^2 * exp(-kappa * t) envelope:

   import numpy as np
   from qutip import coherent, destroy, mesolve, num
   
   tlist = np.linspace(0.0, 50.0, 200)
   a = destroy(20)
   H = 1.0 * a.dag() * a
   c_ops = [np.sqrt(0.1) * a]
   psi0 = coherent(20, 3.0)
   result = mesolve(H, psi0, tlist, c_ops, [num(20)])
   n_t = result.expect[0]
   expected = 9.0 * np.exp(-0.1 * tlist)
   assert np.allclose(n_t, expected, atol=0.5), "photon decay does not match analytic envelope"
   print("damped oscillator: PASS")
   

4. Run the two-qubit entanglement example and confirm concurrence decays monotonically to ~0 and that tensor() produced the correct [[2, 2], [2, 2]] dims:

   from qutip import bell_state, tensor, qeye, sigmaz, mesolve, concurrence
   import numpy as np
   
   psi0 = bell_state("00")
   H = 0.0 * tensor(qeye(2), qeye(2))
   c_ops = [np.sqrt(0.1) * tensor(sigmaz(), qeye(2)),
            np.sqrt(0.1) * tensor(qeye(2), sigmaz())]
   tlist = np.linspace(0.0, 10.0, 100)
   result = mesolve(H, psi0, tlist, c_ops, [], options={"store_states": True})
   concurrences = [concurrence(s) for s in result.states]
   assert concurrences[-1] < 0.1, "concurrence did not decay"
   assert concurrences[0] > 0.9, "initial state not maximally entangled"
   print("entanglement decay: PASS")
   

5. Render a Bloch sphere headless and confirm the file is written:

   from qutip import basis, Bloch
   import matplotlib.pyplot as plt
   
   b = Bloch()
   b.add_states(basis(2, 0))
   b.render()
   fig = plt.gcf()
   fig.savefig("bloch.png", dpi=150)
   plt.close(fig)
   import os; assert os.path.exists("bloch.png"), "bloch.png not written"
   print("bloch render: PASS")
   

6. Call steadystate and confirm it returns without raising the residual-tolerance error:

   import numpy as np
   from qutip import num, destroy, liouvillian, steadystate, operator_to_vector
   
   a = destroy(10)
   H = num(10)
   c_ops = [np.sqrt(0.1) * a]
   L = liouvillian(H, c_ops)
   rho_ss = steadystate(L, method="svd", tol=1e-10)
   residual = (L * operator_to_vector(rho_ss)).norm()
   assert residual < 1e-6, f"steady-state residual {residual:.2e} too large"
   print(f"steady state: PASS (residual={residual:.2e})")
   

Related Skills

• Quantum Information Theory
• Numerical Linear Algebra
• Python Scientific Stack (NumPy, SciPy, Matplotlib)
• Quantum Optics