QuantumComputingMachineLearning

How to assemble, execute, and validate the project Jupyter notebook programmatically (with nbformat), so the notebook stays reproducible and is built the same way every time. Use this skill WHENEVER the task is to create, extend, rebuild, re-run, or check a notebook in this repo (e.g. ResolutionRefPairing.ipynb), add a section/chapter, regenerate its figures, or verify it executes without errors. Always build notebooks with the scripts here rather than editing JSON by hand or pasting large code blobs.

Physics conventions and the validated Python API for the constant-pairing Hamiltonian (nuclear/condensed-matter pairing model). Use this skill WHENEVER the task touches the pairing model, seniority-zero spectra, the delta/g Hamiltonian, Jordan-Wigner encoding of pairs, Hartree-Fock pairing energies, FCI/CCD/UCCSD benchmarks for pairing, or the `pairinglib` package. Consult it before writing any pairing physics so the conventions (level index, qubit ordering, sign of g, benchmark numbers) stay consistent across the notebook, the library, and the paper.

How to build the LaTeX scientific article from the notebook: extract the notebook figures, write/maintain the paper in the project's article style, and compile to PDF. Use this skill WHENEVER the task is to create, edit, or compile the paper (eigenq_pairing.tex), pull figures out of the notebook into paper/figs, update the author list or bibliography, or turn notebook results into article prose. Follow `references/article-conventions.md` for the house style (article class, authblk author block, figure/caption rules).

How the eigenstate-preparation pipeline is built from circuits: gate-based statevector simulation, the Pauli-rotation primitive, gate-level UCCSD and the hardware-efficient ansatz, the prolongation operator, adiabatic resolution refinement, and the rodeo algorithm. Use this skill WHENEVER the task involves VQE, UCCSD/HEA circuits, prolongation, resolution refinement, the adiabatic path, OR the rodeo algorithm (energy scans, eigenstate preparation, the cos^2 success probability, fidelity-vs-M, acceptance). Read `references/rodeo.md` before writing or modifying any rodeo code so the formulas and conventions match the published algorithm.