pmm_tools.models.quasistatic
Real affine microscopic Hamiltonians for independent quasistatic disorder.
All energies and Gaussian widths are micro-eV; the workflow supplies their values through the physical parameter dictionary. Noise coordinates are (mu_L, mu_R, mismatch_L, mismatch_R, dot_mu_or_log_bridge). second_quantization constructs every operator through the existing models.
Functions
| Name | Description |
|---|---|
affine_device | Build fixed real operators once, for broadcast to a worker pool. |
chain_parameters | Hold the nominal t+Delta fixed while applying onsite and mismatch noise. |
disorder_coefficients | Translate standard-normal coordinates into the affine coefficients. |
isolated_chain_operators | Real dense parity operators for batched, noisy Ramsey preparation. |
ramsey_logical_states | Batched isolated-chain ee/oo with an empty dot and a clean phase gauge. |
sobol_gaussian_draws | Read Gaussian coordinates from one scrambled Sobol sequence at an offset. |
sobol_normals | Chain-noise draws with the campaign's dimension and independent scramble seed. |
affine_devicefunction#
def affine_device(n, physical, *, device)Build fixed real operators once, for broadcast to a worker pool.
Dot coefficients: (1, mu_M, common contact hopping, all Gaussian coordinates). Direct coefficients: (1, noisy bridge hopping, chain Gaussian coordinates). Dense small systems store operator matrices; larger systems store their data on one common CSR sparsity pattern. No Hamiltonian projection is performed.
chain_parametersfunction#
def chain_parameters(n, normal, physical)Hold the nominal t+Delta fixed while applying onsite and mismatch noise.
disorder_coefficientsfunction#
def disorder_coefficients(model, normal, control, *, contacts=0.0)Translate standard-normal coordinates into the affine coefficients.
isolated_chain_operatorsfunction#
def isolated_chain_operators(n, physical)Real dense parity operators for batched, noisy Ramsey preparation.
ramsey_logical_statesfunction#
def ramsey_logical_states(model, normal)Batched isolated-chain ee/oo with an empty dot and a clean phase gauge.
sobol_gaussian_drawsfunction#
def sobol_gaussian_draws(dimension, start, count, *, seed)Read Gaussian coordinates from one scrambled Sobol sequence at an offset.
A fixed seed and dimension identify the sequence; power-of-two contiguous batches reproduce a single complete draw. Clipping keeps inverse CDF finite.
sobol_normalsfunction#
def sobol_normals(n, scramble, start, count, *, seed)Chain-noise draws with the campaign's dimension and independent scramble seed.