upstream/K-Dense-AI-scientific-agent-skills/catalogue/skills/qutip/SKILL.md

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 ---
 lineage_type: import
-upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/9c9bd2e9/skills/qutip/SKILL.md
-upstream_sha: 9c9bd2e9
-imported_at: 2026-06-27
-prompt_class: unknown
+upstream_source: https://github.com/K-Dense-AI/scientific-agent-skills/blob/1e5eeffb/skills/qutip/SKILL.md
+upstream_sha: 1e5eeffb
+imported_at: 2026-09-02
+prompt_class: catalogue
 upstream_changes: accepted
 name: qutip
-description: Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.
-license: BSD-3-Clause license
-metadata: {"version": "1.0", "skill-author": "K-Dense Inc."}
+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: "1.2"
+  skill-author: K-Dense Inc.
+  last-reviewed: "2026-07-23"
 ---
 
-# QuTiP: Quantum Toolbox in Python
+# QuTiP 5
 
-## Overview
+## Scope
 
-QuTiP provides comprehensive tools for simulating and analyzing quantum mechanical systems. It handles both closed (unitary) and open (dissipative) quantum systems with multiple solvers optimized for different scenarios.
+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.
 
-## Installation
+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:
 
 ```bash
-uv pip install qutip
+uv venv --python 3.11
+uv pip install "qutip==5.3.0"
 ```
 
-Optional packages for additional functionality:
+For plots:
 
 ```bash
-# Quantum information processing (circuits, gates)
-uv pip install qutip-qip
-
-# Quantum trajectory viewer
-uv pip install qutip-qtrl
+uv pip install "qutip[graphics]==5.3.0"
 ```
 
-## Quick Start
+Optional QuTiP family packages are independently versioned:
+
+```bash
+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:
+
+```python
+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.
 
 ```python
-from qutip import *
 import numpy as np
-import matplotlib.pyplot as plt
+from qutip import basis, mesolve, sigmam, sigmaz
 
-# Create quantum state
-psi = basis(2, 0)  # |0⟩ state
+omega = 2.0
+gamma = 0.15
+tlist = np.linspace(0.0, 20.0, 401)
+excited = basis(2, 0)
 
-# Create operator
-H = sigmaz()  # Hamiltonian
+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": "",
+    },
+)
 
-# Time evolution
-tlist = np.linspace(0, 10, 100)
-result = sesolve(H, psi, tlist, e_ops=[sigmaz()])
-
-# Plot results
-plt.plot(tlist, result.expect[0])
-plt.xlabel('Time')
-plt.ylabel('⟨σz⟩')
-plt.show()
+population = np.asarray(result.e_data["excited"])
+assert np.max(np.abs(population - np.exp(-gamma * tlist))) < 2e-6
+assert isinstance(result.stats, dict)
 ```
 
-## Core Capabilities
+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.
 
-### 1. Quantum Objects and States
+## Time-dependent systems
 
-Create and manipulate quantum states and operators:
+Prefer trusted Pythonic callables or numeric coefficient arrays. Do not create
+coefficient source strings from user input.
 
 ```python
-# States
-psi = basis(N, n)  # Fock state |n⟩
-psi = coherent(N, alpha)  # Coherent state |α⟩
-rho = thermal_dm(N, n_avg)  # Thermal density matrix
+import numpy as np
+from qutip import QobjEvo, sigmax, sigmaz
 
-# Operators
-a = destroy(N)  # Annihilation operator
-H = num(N)  # Number operator
-sx, sy, sz = sigmax(), sigmay(), sigmaz()  # Pauli matrices
+def envelope(t, amplitude, center, width):
+    return amplitude * np.exp(-0.5 * ((t - center) / width) ** 2)
 
-# Composite systems
-psi_AB = tensor(psi_A, psi_B)  # Tensor product
+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)
 ```
 
-**See** `references/core_concepts.md` for comprehensive coverage of quantum objects, states, operators, and tensor products.
+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`.
 
-### 2. Time Evolution and Dynamics
-
-Multiple solvers for different scenarios:
+## Trajectories and stochastic solvers
 
 ```python
-# Closed systems (unitary evolution)
-result = sesolve(H, psi0, tlist, e_ops=[num(N)])
+import numpy as np
+from qutip import basis, mcsolve, sigmam, sigmaz
 
-# Open systems (dissipation)
-c_ops = [np.sqrt(0.1) * destroy(N)]  # Collapse operators
-result = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])
-
-# Quantum trajectories (Monte Carlo)
-result = mcsolve(H, psi0, tlist, c_ops, ntraj=500, e_ops=[num(N)])
+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": ""},
+)
 ```
 
-**Solver selection guide:**
-- `sesolve`: Pure states, unitary evolution
-- `mesolve`: Mixed states, dissipation, general open systems
-- `mcsolve`: Quantum jumps, photon counting, individual trajectories
-- `brmesolve`: Weak system-bath coupling
-- `fmmesolve`: Time-periodic Hamiltonians (Floquet)
+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.
 
-**See** `references/time_evolution.md` for detailed solver documentation, time-dependent Hamiltonians, and advanced options.
-
-### 3. Analysis and Measurement
-
-Compute physical quantities:
+## Steady states, spectra, and phase space
 
 ```python
-# Expectation values
-n_avg = expect(num(N), psi)
+import numpy as np
+from qutip import QFunc, liouvillian, operator_to_vector, qfunc, steadystate
 
-# Entropy measures
-S = entropy_vn(rho)  # Von Neumann entropy
-C = concurrence(rho)  # Entanglement (two qubits)
+rho_ss = steadystate(H, c_ops, method="direct")
+residual = (liouvillian(H, c_ops) * operator_to_vector(rho_ss)).norm()
+assert residual < 1e-9
 
-# Fidelity and distance
-F = fidelity(psi1, psi2)
-D = tracedist(rho1, rho2)
-
-# Correlation functions
-corr = correlation_2op_1t(H, rho0, taulist, c_ops, A, B)
-w, S = spectrum_correlation_fft(taulist, corr)
-
-# Steady states
-rho_ss = steadystate(H, c_ops)
+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))
 ```
 
-**See** `references/analysis.md` for entropy, fidelity, measurements, correlation functions, and steady state calculations.
+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`.
 
-### 4. Visualization
+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`.
 
-Visualize quantum states and dynamics:
+## Advanced boundaries
 
-```python
-# Bloch sphere
-b = Bloch()
-b.add_states(psi)
-b.show()
+- 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.
 
-# Wigner function (phase space)
-xvec = np.linspace(-5, 5, 200)
-W = wigner(psi, xvec, xvec)
-plt.contourf(xvec, xvec, W, 100, cmap='RdBu')
+See `references/advanced.md` for HEOM, Floquet, PIQS, stochastic, and extension
+boundaries.
 
-# Fock distribution
-plot_fock_distribution(psi)
+## Safe local CLIs
 
-# Matrix visualization
-hinton(rho)  # Hinton diagram
-matrix_histogram(H.full())  # 3D bars
+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:
+
+```bash
+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
 ```
 
-**See** `references/visualization.md` for Bloch sphere animations, Wigner functions, Q-functions, and matrix visualizations.
+## Completion checklist
 
-### 5. Advanced Methods
-
-Specialized techniques for complex scenarios:
-
-```python
-# Floquet theory (periodic Hamiltonians)
-T = 2 * np.pi / w_drive
-f_modes, f_energies = floquet_modes(H, T, args)
-result = fmmesolve(H, psi0, tlist, c_ops, T=T, args=args)
-
-# HEOM (non-Markovian, strong coupling)
-from qutip.nonmarkov.heom import HEOMSolver, BosonicBath
-bath = BosonicBath(Q, ck_real, vk_real)
-hsolver = HEOMSolver(H_sys, [bath], max_depth=5)
-result = hsolver.run(rho0, tlist)
-
-# Permutational invariance (identical particles)
-psi = dicke(N, j, m)  # Dicke states
-Jz = jspin(N, 'z')  # Collective operators
-```
-
-**See** `references/advanced.md` for Floquet theory, HEOM, permutational invariance, stochastic solvers, superoperators, and performance optimization.
-
-## Common Workflows
-
-### Simulating a Damped Harmonic Oscillator
-
-```python
-# System parameters
-N = 20  # Hilbert space dimension
-omega = 1.0  # Oscillator frequency
-kappa = 0.1  # Decay rate
-
-# Hamiltonian and collapse operators
-H = omega * num(N)
-c_ops = [np.sqrt(kappa) * destroy(N)]
-
-# Initial state
-psi0 = coherent(N, 3.0)
-
-# Time evolution
-tlist = np.linspace(0, 50, 200)
-result = mesolve(H, psi0, tlist, c_ops, e_ops=[num(N)])
-
-# Visualize
-plt.plot(tlist, result.expect[0])
-plt.xlabel('Time')
-plt.ylabel('⟨n⟩')
-plt.title('Photon Number Decay')
-plt.show()
-```
-
-### Two-Qubit Entanglement Dynamics
-
-```python
-# Create Bell state
-psi0 = bell_state('00')
-
-# Local dephasing on each qubit
-gamma = 0.1
-c_ops = [
-    np.sqrt(gamma) * tensor(sigmaz(), qeye(2)),
-    np.sqrt(gamma) * tensor(qeye(2), sigmaz())
-]
-
-# Track entanglement
-def compute_concurrence(t, psi):
-    rho = ket2dm(psi) if psi.isket else psi
-    return concurrence(rho)
-
-tlist = np.linspace(0, 10, 100)
-result = mesolve(qeye([2, 2]), psi0, tlist, c_ops)
-
-# Compute concurrence for each state
-C_t = [concurrence(state.proj()) for state in result.states]
-
-plt.plot(tlist, C_t)
-plt.xlabel('Time')
-plt.ylabel('Concurrence')
-plt.title('Entanglement Decay')
-plt.show()
-```
-
-### Jaynes-Cummings Model
-
-```python
-# System parameters
-N = 10  # Cavity Fock space
-wc = 1.0  # Cavity frequency
-wa = 1.0  # Atom frequency
-g = 0.05  # Coupling strength
-
-# Operators
-a = tensor(destroy(N), qeye(2))  # Cavity
-sm = tensor(qeye(N), sigmam())  # Atom
-
-# Hamiltonian (RWA)
-H = wc * a.dag() * a + wa * sm.dag() * sm + g * (a.dag() * sm + a * sm.dag())
-
-# Initial state: cavity in coherent state, atom in ground state
-psi0 = tensor(coherent(N, 2), basis(2, 0))
-
-# Dissipation
-kappa = 0.1  # Cavity decay
-gamma = 0.05  # Atomic decay
-c_ops = [np.sqrt(kappa) * a, np.sqrt(gamma) * sm]
-
-# Observables
-n_cav = a.dag() * a
-n_atom = sm.dag() * sm
-
-# Evolve
-tlist = np.linspace(0, 50, 200)
-result = mesolve(H, psi0, tlist, c_ops, e_ops=[n_cav, n_atom])
-
-# Plot
-fig, axes = plt.subplots(2, 1, figsize=(8, 6), sharex=True)
-axes[0].plot(tlist, result.expect[0])
-axes[0].set_ylabel('⟨n_cavity⟩')
-axes[1].plot(tlist, result.expect[1])
-axes[1].set_ylabel('⟨n_atom⟩')
-axes[1].set_xlabel('Time')
-plt.tight_layout()
-plt.show()
-```
-
-## Tips for Efficient Simulations
-
-1. **Truncate Hilbert spaces**: Use smallest dimension that captures dynamics
-2. **Choose appropriate solver**: `sesolve` for pure states is faster than `mesolve`
-3. **Time-dependent terms**: String format (e.g., `'cos(w*t)'`) is fastest
-4. **Store only needed data**: Use `e_ops` instead of storing all states
-5. **Adjust tolerances**: Balance accuracy with computation time via `Options`
-6. **Parallel trajectories**: `mcsolve` automatically uses multiple CPUs
-7. **Check convergence**: Vary `ntraj`, Hilbert space size, and tolerances
-
-## Troubleshooting
-
-**Memory issues**: Reduce Hilbert space dimension, use `store_final_state` option, or consider Krylov methods
-
-**Slow simulations**: Use string-based time-dependence, increase tolerances slightly, or try `method='bdf'` for stiff problems
-
-**Numerical instabilities**: Decrease time steps (`nsteps` option), increase tolerances, or check Hamiltonian/operators are properly defined
-
-**Import errors**: Ensure QuTiP is installed correctly; quantum gates require `qutip-qip` package
+- 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
 
-This skill includes detailed reference documentation:
+- `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
 
-- **`references/core_concepts.md`**: Quantum objects, states, operators, tensor products, composite systems
-- **`references/time_evolution.md`**: All solvers (sesolve, mesolve, mcsolve, brmesolve, etc.), time-dependent Hamiltonians, solver options
-- **`references/visualization.md`**: Bloch sphere, Wigner functions, Q-functions, Fock distributions, matrix plots
-- **`references/analysis.md`**: Expectation values, entropy, fidelity, entanglement measures, correlation functions, steady states
-- **`references/advanced.md`**: Floquet theory, HEOM, permutational invariance, stochastic methods, superoperators, performance tips
+## Dated official sources
 
-## External Resources
+Verified **2026-07-23**:
 
-- Documentation: https://qutip.readthedocs.io/
-- Tutorials: https://qutip.org/qutip-tutorials/
-- API Reference: https://qutip.readthedocs.io/en/stable/apidoc/apidoc.html
-- GitHub: https://github.com/qutip/qutip
+- [QuTiP 5.3.0 PyPI metadata](https://pypi.org/project/qutip/)
+- [QuTiP 5.3.0 release](https://github.com/qutip/qutip/releases/tag/v5.3.0)
+- [QuTiP 5.3 changelog](https://qutip.readthedocs.io/en/stable/changelog.html)
+- [QuTiP 5.3 API](https://qutip.readthedocs.io/en/stable/apidoc/apidoc.html)
+- [QuTiP version-5 tutorials](https://github.com/qutip/qutip-tutorials/tree/main/tutorials-v5)
+- [qutip-qip PyPI](https://pypi.org/project/qutip-qip/)
+- [qutip-qtrl PyPI](https://pypi.org/project/qutip-qtrl/)
+- [qutip-jax PyPI](https://pypi.org/project/qutip-jax/)
+- [official unreleased qutip-cupy repository](https://github.com/qutip/qutip-cupy)
 
+## Citing Scientific Agent Skills
+
+This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a
+manuscript, report, presentation, or code release, add the paper to the references or
+software section and tell the user you did so:
+
+> Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent
+> Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065.
+> https://doi.org/10.48550/arXiv.2609.00065
+
+Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the
+latest arXiv version, so never append a version suffix such as `v1`. When network access is
+available, fetch https://arxiv.org/abs/2609.00065 (or
+http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take
+the author list, year, and version from that record. If the record lists a journal reference
+or publisher DOI, cite the published version instead.