[WIP] Add the adjoint method #83
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@@ Coverage Diff @@
## master #83 +/- ##
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- Coverage 98.14% 79.71% -18.44%
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Files 3 3
Lines 54 69 +15
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+ Hits 53 55 +2
- Misses 1 14 +13
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| false, | ||
| obsWires[i].size() | ||
| ); | ||
| lambdas.push_back(phiCopy); |
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So we know how large lambdas is going in. Why not preallocate and fill? Is there a reason we need vector functionality?
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Ok. I think I see. lambdas is a vector of statevectors? Why not just have an array with additional dimension?
| throw std::invalid_argument("The operation is not supported using the adjoint differentiation method"); | ||
| } else if ((operations[i] != "QubitStateVector") && (operations[i] != "BasisState")) { | ||
| // copy |phi> to |mu> before applying Uj* | ||
| CplxType* phiCopyArr = new CplxType[phi.length]; |
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Could you just allocate the memory once and then always copy the phi statevector there, overwriting it's value from the last loop?
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| # send in flattened array of zeros to be populated by adjoint_jacobian | ||
| jac = np.zeros(len(tape.observables) * len(tape.trainable_params)) | ||
| adjoint_jacobian( |
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Maybe have this named something slightly different than the method?
| opWires[i].size() | ||
| ); | ||
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| if (std::find(trainableParams.begin(), trainableParams.end(), paramNumber) != trainableParams.end()) { |
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I think here you could take advantage of the fact that trainableParams is ordered and paramNumber is continuously decreasing.
You could have a next_trainableParam_index and compare trainableParams[next_trainableParam_index] to paramNumber. If paramNumber is trainable, you decrease next_trainable_param_index.
Adds the adjoint method to
lightning.qubit.