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Hybrid Quantum-Classical Machine Learning in TensorFlow
The impression I got from tutorials was that in general, other than for random gates, the convention is that quantum circuit ansatz are constructed manually. (If I am mistaken about...
With early [stages of CUDA support starting to make its way into qsim](https://github.com/quantumlib/qsim/pull/366) we should investigate registering GPU host devices for our OP kernels so we can give our users...
In the TFQ sync today there was talk of potential new features, and I wanted to suggest something that I think would be useful (to me and the larger community):...
Currently the Adjoint gradient method in C++ uses finite difference of gates to calculate the "gradient gate" to apply to the state. In terms of numerical precision this isn't ideal....
It would be a very useful feature to be able to reason about "Circuit structures". Supporting this feature would mean that we would roughly need to be able create an...
Certain places in our codebase still use `TransformRangeConcurrently`. It would be good to get rid of this in favor of `ParallelFor` so that we can also get rid of `core/ops/tfq_simulate_utils.cc`
Are there functionalities in TFQ that allow one to specify an arbitrary input state in the 2^N Hilbert space? I understand that one could in principle make a circuit to...
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Hybrid Quantum-Classical Machine Learning in TensorFlow