raw_feature_vector

class raw_feature_vector(feature_dimension)

Bases:

The raw feature vector circuit.

This circuit acts as parameterized initialization for statevectors with feature_dimension dimensions, thus with log2(feature_dimension) qubits. The circuit contains a placeholder instruction that can only be synthesized/defined when all parameters are bound.

In ML, this circuit can be used to load the training data into qubit amplitudes. It does not apply an kernel transformation (therefore, it is a “raw” feature vector).

Since initialization is implemented via a QuantumCircuit.initialize() call, this circuit can’t be used with gradient based optimizers, one can see a warning that gradients can’t be computed.

Examples:

from qiskit_machine_learning.circuit.library import RawFeatureVector
circuit = RawFeatureVector(4)
print(circuit.num_qubits)
# prints: 2

print(circuit.draw(output='text'))
# prints:
#      ┌───────────────────────────────────────────────┐
# q_0: ┤0                                              ├
#      │  Parameterizedinitialize(x[0],x[1],x[2],x[3]) │
# q_1: ┤1                                              ├
#      └───────────────────────────────────────────────┘

print(circuit.ordered_parameters)
# prints: [Parameter(p[0]), Parameter(p[1]), Parameter(p[2]), Parameter(p[3])]

import numpy as np
state = np.array([1, 0, 0, 1]) / np.sqrt(2)
bound = circuit.assign_parameters(state)
print(bound)
# prints:
#      ┌───────────────────────────────────────────────┐
# q_0: ┤0                                              ├
#      │  Parameterizedinitialize(0.70711,0,0,0.70711) │
# q_1: ┤1                                              ├
#      └───────────────────────────────────────────────┘

Parameters:

feature_dimension (int) – The feature dimension from which the number of qubits is inferred as n_qubits = log2(feature_dim)

Raises:

ValueError – If feature_dimension is not a power of 2.

Returns:

The raw feature

Return type:

QuantumCircuit