Add support for the new VQE workflows to the differentiable batch execution pipeline #1608
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josh146
added
review-ready 👌
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[ch8557] |
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@josh146 only minor comments, this will be such a nice feature to have! 💯
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| a, b, c = coeffs | ||
| x, y = weights | ||
| tape.trainable_params = {0, 1, 2, 4} |
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Any reason it is not using all 3 coeffs of H?
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Oh... very good question 🤔 I remember doing this deliberately, but for the life of me cannot remember the reason!
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Oh! Sorry I misread this line. At first I thought it was ==, but now I realize it is an assignment =.
Basically, I want to test the case where not all Hamiltonian coefficients are trainable, just to make the test more complicated.
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| self._trainable_params = param_indices | |||
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| def get_operation(self, idx): | |||
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Just a thought; could name this get_trainable_operation?
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@anthayes92 that was my original name 😆
Oddly, I found myself getting confused while writing this, and decided it was better to mirror the existing tape.get_parameters() method (which by default only gets trainable parameters).
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Awesome addition @josh146 ! Very thorough testing |
Co-authored-by: Olivia Di Matteo <2068515+glassnotes@users.noreply.github.com>
Context: In #1483, #1596, and #1551, support was added for:
expval(H)inside of a QNodedefault.qubit, to support Hamiltonians directly.However, these changes were not ported over to the new differentiable batch-execute pipeline.
Description of the Change:
Add support for differentiating (one or more) Hamiltonians to
qml.gradients.param_shift.Modify
hamiltonian_gradto support multiple Hamiltonians (return expval(H1), expval(H2)), as this is supported execution behaviour.Fixes a couple of bugs with the Autograd and TensorFlow batch interfaces
Adds tests
Benefits: returning
expval(H)from QNodes is now supported within the new batch-differentiable pipeline.Possible Drawbacks: n/a
Related GitHub Issues: n/a