quantum
quantum copied to clipboard
tensorflow
Hybrid Quantum-Classical Machine Learning in TensorFlow
A solution to potential data leakage in #828. Instead of concatenating train and test sets, they should be separately dealt with when getting a stilted dataset: In lines [L745-752](https://github.com/OkuyanBoga/quantum/blob/61da8ecf69a371999845b214f20d22b20a948a9d/docs/tutorials/quantum_data.ipynb#L868-L869): ```...
There doesn't seem to be an installable copy of `tensorflow-quantum` from PyPI for Apple Silicon Macs. We need to add this configuration to the internal Google workflow that builds &...
Cirq 1.4.x has been out for a while, and we should update to it. If 1.5.0 comes out before we release TFQ 0.7.4, we should support that.
`configure.sh` asks about CPU versus GPU, but then doesn't use the cpu version of the TFQ Python package if the user chooses the CPU option.
It would be good to add a YAML file linter workflow to the codebase. [Yamllint](https://yamllint.readthedocs.io/en/stable/) is a good candidate, and there exists [at least one GHA](https://github.com/ibiqlik/action-yamllint) action for running `yamllint`...
In the documentation of some functions, e.g. PQC, Expectation, while there is a lot of nice description of the functionality, it would also be much more convenient for there to...
Example ```python c = cirq.Circuit() qubits = cirq.GridQubit.rect(2, 1) c.append(cirq.rz(0.)(qubits[0])) c.append(cirq.H(qubits[1])) sampled_expectation_layer = tfq.layers.SampledExpectation() output = sampled_expectation_layer( [c], operators=[cirq.Z(qubits[0]), cirq.Z(qubits[1]), cirq.Z(qubits[0]) * cirq.Z(qubits[1])], repetitions=1) print(output) # we have [1, -1,...
Both first order derivative of tfq layers and higher order gradient of non tfq layers work with ``GradientTape`` as follows: ```python x = tf.Variable(initial_value=0.2) with tf.GradientTape() as t: with tf.GradientTape()...
Our use case involves using a Sample layer to obtain bitstrings, then computing a loss as a function of these bitstrings. We wish to obtain gradients of the loss with...
Metadata
Owner
Metadata
Hybrid Quantum-Classical Machine Learning in TensorFlow