Module: tfq

Module functions for tensorflow_quantum.*


datasets module: Experimental location for interesting quantum datasets.

differentiators module: Module functions for tfq.differentiators.*

layers module: Module definitions for tensorflow_quantum.python.layers.*

math module: Module for tfq.core.ops.math_ops.*

noise module: Module for tfq.core.ops.noise.*

optimizers module: Module definitions for tensorflow_quantum.python.optimizers.*

util module: A collection of helper functions which are useful in places in TFQ.


append_circuit(...): Merge programs in the input tensors.

convert_to_tensor(...): Convert lists of tfq supported primitives to tensor representations.

from_tensor(...): Convert a tensor of tfq primitives back to Python objects.

get_expectation_op(...): Get a TensorFlow op that will calculate batches of expectation values.

get_quantum_concurrent_op_mode(...): Get the global op latency mode from execution context.

get_sampled_expectation_op(...): Get a TensorFlow op that will calculate sampled expectation values.

get_sampling_op(...): Get a Tensorflow op that produces samples from given quantum circuits.

get_state_op(...): Get a TensorFlow op that produces states from given quantum circuits.

get_unitary_op(...): Get an op that calculates the unitary matrix for the given circuits.

padded_to_ragged(...): Utility tf.function that converts a padded tensor to ragged.

padded_to_ragged2d(...): Utility tf.function that converts a 2d padded tensor to ragged.

resolve_parameters(...): Replace symbols in a batch of programs with concrete values.

set_quantum_concurrent_op_mode(...): Set the global op latency mode in execution context.

version '0.7.2'