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Module functions for tensorflow_quantum.*
Modules
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.
Functions
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.
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| version |
'0.7.2'
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