Circuit cutting: add interface integration tests #2231
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[sc-14724] |
Codecov Report
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## master #2231 +/- ##
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Coverage 99.26% 99.26%
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Files 231 231
Lines 18349 18349
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Hits 18215 18215
Misses 134 134 Continue to review full report at Codecov.
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trbromley
changed the title
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| res = cut_circuit(x) | ||
| res_expected = circuit(x) | ||
| assert np.isclose(res.detach().numpy(), res_expected.detach().numpy()) |
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Is this the detach method being used here?
Why is it needed for comparisons?
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If you do assert np.isclose(res, res_expected) then PyTorch gives the error:
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
Could possibly also use torch.isclose.
| return qml.expval(qml.PauliZ(wires=[0])) | ||
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| x = torch.tensor(0.531, requires_grad=True) | ||
| cut_circuit = qcut.cut_circuit(circuit, use_opt_einsum=use_opt_einsum) |
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is qcut.cut_circuit preferred over qml.transforms.cut_circuit?
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(flyby comment: why not make it top-level, @qml.cut_circuit?)
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is qcut.cut_circuit preferred over qml.transforms.cut_circuit?
For sure in user-facing documentation we don't want to go with qcut and should instead go through qml.
(flyby comment: why not make it top-level, @qml.cut_circuit?)
Yes good idea, could do! What is the criteria for qml.func() vs qml.transforms.func()? It looks like it's grown a bit organically so far with both choices used.
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What is the criteria for qml.func() vs qml.transforms.func()? It looks like it's grown a bit organically so far with both choices used.
There is no criteria at the moment, it is done depending on UI considerations, and 'how important' a particular transform is!
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| res = cut_circuit(x) | ||
| res_expected = circuit(x) | ||
| assert np.isclose(res.detach().numpy(), res_expected.detach().numpy()) |
There was a problem hiding this comment.
If you do assert np.isclose(res, res_expected) then PyTorch gives the error:
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
Could possibly also use torch.isclose.
| return qml.expval(qml.PauliZ(wires=[0])) | ||
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| x = torch.tensor(0.531, requires_grad=True) | ||
| cut_circuit = qcut.cut_circuit(circuit, use_opt_einsum=use_opt_einsum) |
There was a problem hiding this comment.
is qcut.cut_circuit preferred over qml.transforms.cut_circuit?
For sure in user-facing documentation we don't want to go with qcut and should instead go through qml.
(flyby comment: why not make it top-level, @qml.cut_circuit?)
Yes good idea, could do! What is the criteria for qml.func() vs qml.transforms.func()? It looks like it's grown a bit organically so far with both choices used.
Context:
Adds tests for the
cut_circuittransform that ensures differentiability with all of the interfaces.