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Hybrid Quantum-Classical Machine Learning in TensorFlow
To enable more consistency in tests, would be useful to allow seeds to be entered into the utility module test functions.
Running some parameterized tests that use `random_symbol_circuit`, but get a > ZeroDivisionError: integer division or modulo by zero when the case of no symbols arises. Low priority but wanted to...
While there are still problems with the rendering of the new math ops (see https://github.com/tensorflow/quantum/issues/594), I found what I think is another documentation problem on the tfq website. The descriptions...
During the writing of #621 it became apparent that the new version of cirq 0.13.0 had changed the way gates are decomposed. Past quantumlib/Cirq#4167 some gates were decomposed into 3...
We need to move it. https://github.com/github/renaming
Does tfq currently support circuit simulation with float64/complex128 precision? At least the code demo below returns me a float32 tensor. ```python import tensorflow_quantum as tfq import cirq import numpy as...
Currently using channels (noise or reset, either way non-unitary) is not supported in the fidelity operation. Perhaps the fidelity could be modified or a "noisy fidelity" could be added which...
# Build the quantum model layer theta = sympy.Symbol('theta') q_model = cirq.Circuit(cirq.ry(theta)(qubit)) q_data_input = tf.keras.Input( shape=(), dtype=tf.dtypes.string) expectation = tfq.layers.PQC(q_model, cirq.Z(qubit)) expectation_output = expectation(q_data_input) # Attach the classical SoftMax classifier...