Evaluate compiled circuit with batched parameter values.
Each term family (NodePhases, HalfPiPhases, PiProducts, PhasePairs) computes its own contribution via .evaluate(param_vals). This function multiplies those together with the per-graph ScalarPrefactor and folds in power2 / any approximate floatfactor.
Parameters:
| Name | Type | Description | Default |
circuit | CompiledScalarGraphs | Compiled circuit representation. | required |
param_vals | Array | Binary parameter values (error bits + measurement/detector outcomes), shape (batch_size, n_params). | required |
Returns:
| Type | Description |
Array | Complex array of shape (batch_size,) — the per-sample amplitude. |
Source code in src/tsim/compile/evaluate.py
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59 | @jax.jit
def evaluate(circuit: CompiledScalarGraphs, param_vals: Array) -> Array:
"""Evaluate compiled circuit with batched parameter values.
Each term family (``NodePhases``, ``HalfPiPhases``, ``PiProducts``,
``PhasePairs``) computes its own contribution via ``.evaluate(param_vals)``.
This function multiplies those together with the per-graph
``ScalarPrefactor`` and folds in ``power2`` / any approximate floatfactor.
Args:
circuit: Compiled circuit representation.
param_vals: Binary parameter values (error bits + measurement/detector
outcomes), shape ``(batch_size, n_params)``.
Returns:
Complex array of shape ``(batch_size,)`` — the per-sample amplitude.
"""
prefactor = circuit.prefactor
if prefactor.phase_indices.shape[0] == 0:
return jnp.zeros(param_vals.shape[0], dtype=jnp.complex64)
static_phases = ExactScalarArray(UNIT_PHASES[prefactor.phase_indices])
float_factor = ExactScalarArray(prefactor.floatfactor)
total = functools.reduce(
operator.mul,
[
circuit.node_phases.evaluate(param_vals),
circuit.halfpi_phases.evaluate(param_vals),
circuit.pi_products.evaluate(param_vals),
circuit.phase_pairs.evaluate(param_vals),
static_phases,
float_factor,
],
)
if not prefactor.has_approximate_floatfactors:
total = ExactScalarArray(total.coeffs, total.power + prefactor.power2)
return total.sum().to_complex()
return jnp.sum(
total.to_complex() * prefactor.approximate_floatfactors * 2.0**prefactor.power2,
axis=-1,
)
|