BPResult

class pyqit.utils.diagnostic.BPResult(n_qubits: int, n_samples: int, layer_variances: dict[str, float], layer_ratios: dict[str, float], overall_variance: float, expected_variance: float, is_barren: bool, quantum_variance: float, classical_variance: float | None = None, n_executions: int | None = None)

Bases: object

Result of check_barren_plateau.

repr(result) or print(result) renders a table (rich if installed, ASCII otherwise).

n_qubits, n_samples

Type:

int

layer_variances, layer_ratios

Per-weight-layer gradient variance, and its ratio to the baseline.

Type:

dict of {str: float}

overall_variance, quantum_variance

Type:

float

expected_variance

Theoretical floor from McClean et al., scaled by bp_scale_factor.

Type:

float

is_barren

Type:

bool

classical_variance

Set only for hybrid models with classical weight layers.

Type:

float, optional

n_executions

Circuit executions the sampling cost, as counted by the device: one per sample under backprop, one plus two per parameter under parameter-shift. None when the model runs no QNode.

Type:

int, optional