Functions
def	_kron_n (ops)

def	_python_shot_noise_z_estimator (z_terms, shots, p_readout, seed=0)

def	generate_hamiltonian (n)

def	generate_hamiltonian_tmp (n)

def	generate_zz_xx_hamiltonian (n_qubits, h=0.5, topology=None, Jz=1.0, Jx=1.0, Jy=1.0)

def	is_hermitian (H, tol=1e-10)

def	matrices_differ (A, B, tol=1e-12)

def	test_generate_hamiltonian_per_qubit_h ()

def	test_generate_hamiltonian_returns_sparse_or_ndarray ()

def	test_generate_zz_xx_hamiltonian_parameter_dependence ()

def	test_hamiltonian_shape_and_hermitian (n)

def	test_hamiltonian_spectrum_real (n)

def	test_oplist_indices_in_range ()

def	test_wrapper_shot_noise_against_analytic ()

def	test_z_only_shot_noise_analytic_and_simulation ()

def	to_dense_if_small (H, max_dim=1<< 6)

Variables
	_I

	_X

	_Y

	_Z

	dtype

	generate_zz_xx_hamiltonian

	H

	heis_mod

	heis_path

	n

	repo_root

	spec

	state

Detailed Description

Unit tests for Hamiltonian helpers and shot-noise example plumbing.

This module contains small, fast tests intended for CI that validate the
construction and basic properties of Heisenberg-style Hamiltonians used by
the VQE examples. Tests are written to be robust when the native C++
extension is not available: the test runner first attempts to import the
real implementations from ``examples/VQE/Heisenberg_VQE.py`` and falls back
to lightweight Python-only generators if the import fails. This keeps CI
fast while still exercising the core logic.

Checks performed include:
- Hamiltonian shape and Hermiticity for small systems
- Eigenvalue reality for small dense matrices
- Sensitivity of the generator to coupling parameters (Jx/Jz/Jy)
- API contract for ``generate_hamiltonian`` (returns object with ``.shape``)

Function Documentation

◆ _kron_n()

def tests.VQE.test_shot_noise_measurement._kron_n ( ops )

private

Definition at line 68 of file test_shot_noise_measurement.py.

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◆ _python_shot_noise_z_estimator()

def tests.VQE.test_shot_noise_measurement._python_shot_noise_z_estimator	(	z_terms,
		shots,
		p_readout,
		seed = `0`
	)

private

Simple python Monte-Carlo estimator for Z-only Hamiltonians on the
all-|0> state. Assumes measurements are independent and readout flips
each measurement bit with probability `p_readout`.
Returns (mean_estimate, sample_variance_of_estimates).

Definition at line 242 of file test_shot_noise_measurement.py.

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◆ generate_hamiltonian()

def tests.VQE.test_shot_noise_measurement.generate_hamiltonian ( n )

Definition at line 33 of file test_shot_noise_measurement.py.

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◆ generate_hamiltonian_tmp()

def tests.VQE.test_shot_noise_measurement.generate_hamiltonian_tmp ( n )

Definition at line 29 of file test_shot_noise_measurement.py.

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◆ generate_zz_xx_hamiltonian()

def tests.VQE.test_shot_noise_measurement.generate_zz_xx_hamiltonian	(	n_qubits,
		h = `0.5`,
		topology = `None`,
		Jz = `1.0`,
		Jx = `1.0`,
		Jy = `1.0`
	)

Definition at line 79 of file test_shot_noise_measurement.py.

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◆ is_hermitian()

def tests.VQE.test_shot_noise_measurement.is_hermitian	(	H,
		tol = `1e-10`
	)

Check Hermiticity (H == H^â ) up to `tol`.

Returns (is_hermitian_bool, max_abs_deviation) so tests can give
informative failures.

Definition at line 132 of file test_shot_noise_measurement.py.

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◆ matrices_differ()

def tests.VQE.test_shot_noise_measurement.matrices_differ	(	A,
		B,
		tol = `1e-12`
	)

Return True if matrices A and B differ by more than `tol`.

Accepts either sparse or dense inputs; for sparse results the
function compares stored values, otherwise `np.allclose` is used.

Definition at line 179 of file test_shot_noise_measurement.py.

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◆ test_generate_hamiltonian_per_qubit_h()

def tests.VQE.test_shot_noise_measurement.test_generate_hamiltonian_per_qubit_h ( )

Pass an array for `h` and verify per-qubit fields are represented.

Definition at line 231 of file test_shot_noise_measurement.py.

◆ test_generate_hamiltonian_returns_sparse_or_ndarray()

def tests.VQE.test_shot_noise_measurement.test_generate_hamiltonian_returns_sparse_or_ndarray ( )

Confirm `generate_hamiltonian` returns either a dense ndarray or a
scipy sparse matrix with the expected 2^n shape (API contract test).

Definition at line 207 of file test_shot_noise_measurement.py.

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◆ test_generate_zz_xx_hamiltonian_parameter_dependence()

def tests.VQE.test_shot_noise_measurement.test_generate_zz_xx_hamiltonian_parameter_dependence ( )

Ensure that changing coupling parameters (e.g. Jx) modifies the
produced Hamiltonian. This guards against parameter-ignored bugs.

Definition at line 197 of file test_shot_noise_measurement.py.

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◆ test_hamiltonian_shape_and_hermitian()

def tests.VQE.test_shot_noise_measurement.test_hamiltonian_shape_and_hermitian ( n )

Verify generated Hamiltonian has the expected shape (2^n x 2^n)
and is Hermitian up to numerical tolerance. This is a basic
correctness check for the Hamiltonian generator.

Definition at line 151 of file test_shot_noise_measurement.py.

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◆ test_hamiltonian_spectrum_real()

def tests.VQE.test_shot_noise_measurement.test_hamiltonian_spectrum_real ( n )

Check that eigenvalues are numerically real for small systems.

Hermitian matrices must have real eigenvalues; this catches subtle
bugs in matrix construction where non-Hermitian entries creep in.

Definition at line 164 of file test_shot_noise_measurement.py.

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◆ test_oplist_indices_in_range()

def tests.VQE.test_shot_noise_measurement.test_oplist_indices_in_range ( )

All indices in the produced `oplist` must be within [0, n_qubits-1].

Definition at line 219 of file test_shot_noise_measurement.py.

◆ test_wrapper_shot_noise_against_analytic()

def tests.VQE.test_shot_noise_measurement.test_wrapper_shot_noise_against_analytic ( )

If the C++ wrapper is importable, run a small-shot comparison of its
shot-noise estimator against the analytic Z-only result. Skips when the
native extension is not present.

Definition at line 299 of file test_shot_noise_measurement.py.

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◆ test_z_only_shot_noise_analytic_and_simulation()

def tests.VQE.test_shot_noise_measurement.test_z_only_shot_noise_analytic_and_simulation ( )

Verify analytic formulas for Z-only Hamiltonians against Monte-Carlo
simulation (Python implementation). This does not require the C++
extension and tests statistical behavior of the estimator.

Definition at line 272 of file test_shot_noise_measurement.py.

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◆ to_dense_if_small()

def tests.VQE.test_shot_noise_measurement.to_dense_if_small	(	H,
		max_dim = `1 << 6`
	)

Return a dense array for small sparse matrices, otherwise return
the original sparse object. This keeps tests fast while allowing
checks that require dense operations for small sizes.

Definition at line 119 of file test_shot_noise_measurement.py.

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