"""The module which houses the Parametrized Quantum Circuit trainer class.
It generates the TensorFlow Quantum model, and allows Keras like API to
train and evaluate a model.
"""
import typing
import cirq
import tqdm.auto as tqdm
import warnings
warnings.filterwarnings("ignore")
import tensorflow as tf
import tensorflow_quantum as tfq
from ..interface.metas import AnalyzerList
from ..interface.circuit import CircuitDescriptor
[docs]class PQCSimulatedTrainer:
"""A class to train parametrized Quantum Circuits in Tensorflow Quantum
Uses gradient descent over the provided parameters, using the TFQ Adjoin differentiator.
"""
def __init__(self, circuit: CircuitDescriptor):
"""Constructs a PQC Trainer object to train the circuit.
:type circuit: CircuitDescriptor
:param circuit: The circuit object to train on the loss function
"""
self.optimizer = tf.keras.optimizers.Adam(lr=0.01)
self.pqc_layer = tfq.layers.PQC(
circuit.cirq_circuit,
circuit.cirq_cost,
differentiator=tfq.differentiators.Adjoint(),
)
self.model = tf.keras.models.Sequential(
[tf.keras.layers.Input(shape=(), dtype=tf.dtypes.string), self.pqc_layer]
)
self.circuit = circuit
[docs] def train(
self, n_samples=100, loggers: typing.Optional[AnalyzerList] = None
) -> tf.keras.Model:
"""Trains the parameter of the circuit to minimize the loss.
:type n_samples: int
:param n_samples: Number of samples to train the circuit over
:type loggers: `AnalyzerList`
:param loggers: The AnalyzerList that tracks the training of the model
:returns: The trained model
:rtype: tf.keras.Model
"""
dummy_input = tfq.convert_to_tensor([cirq.Circuit()])
total_error = 0.0
with tqdm.trange(n_samples) as iterator:
iterator.set_description("QAOA Optimization Loop")
for step in iterator:
with tf.GradientTape() as tape:
error = self.model(dummy_input)
grads = tape.gradient(error, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables)
)
error = error.numpy()[0][0]
if loggers is not None:
loggers.log(self, error)
total_error += error
iterator.set_postfix(error=total_error / (step + 1))
return self.model
[docs] def evaluate(self, n_samples: int = 1000) -> float:
"""Evaluates the Parametrized Quantum Circuit.
:type n_samples: int
:param n_samples: The number of samples to evaluate the circuit over
:returns: The average loss of the circuit over all the samples
:rtype: float
"""
dummy_input = tfq.convert_to_tensor([cirq.Circuit()])
total_error = 0.0
with tqdm.trange(n_samples) as iterator:
iterator.set_description("QAOA Evaluation Loop")
for step in iterator:
error = self.model(dummy_input)
error = error.numpy()[0][0]
total_error += error
iterator.set_postfix(error=total_error / (step + 1))
return total_error / n_samples