qward.metrics.quantum_specific_metrics.QuantumSpecificMetrics¶

class QuantumSpecificMetrics(circuit)[source]¶

Extract intrinsically quantum metrics from QuantumCircuit objects.

Quantum-specific metrics provide insight into the true quantum behavior of a circuit, its resource requirements, and its expected difficulty for classical simulation. They complement structural and element-level metrics by focusing exclusively on quantum effects arising from the circuit’s transformations and state evolution.

circuit¶

The quantum circuit to analyze (inherited from MetricCalculator).

Type:: QuantumCircuit

_circuit_dag¶

DAG representation of the circuit, used for structural traversal when computing quantum-specific metrics.

Type:: DAGCircuit | None

_torch_available¶

Indicates whether PyTorch is available for metrics that rely on differentiable simulation or gradient-based optimization.

Type:: bool

_max_steps¶

Maximum number of optimization steps used in iterative or differentiable metrics (e.g., magic or sensitivity estimation).

Type:: int

_lr¶

Learning rate used for gradient-based optimization of certain metrics.

Type:: float

_device¶

Device specification (“cpu” or “cuda”) for metrics that may leverage PyTorch computation.

Type:: str

_use_trace_norm¶

Whether to use the trace norm when computing specific quantum sensitivity or distance-based metrics.

Type:: bool

Inicializa el calculador de métricas cuánticas específicas.

__init__(circuit)[source]¶: Inicializa el calculador de métricas cuánticas específicas.

Methods

`__init__`(circuit)	Inicializa el calculador de métricas cuánticas específicas.
`get_metrics`()	Calculate and return quantum specific metrics.
`is_ready`()	Check if the metric is ready to be calculated.

Attributes

`circuit`	Get the quantum circuit.
`id`	Get the ID of the metric.
`metric_type`	Get the type of this metric.
`name`	Get the name of the metric.