Comments (3)
mariaschuld
commented on August 20, 2024
Happy to help coding something up if needed!
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josh146
commented on August 20, 2024
I agree with having a GD optimizer that works without data if possible 👍
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co9olguy
commented on August 20, 2024
I discussed with @mariaschuld about this and she will proceed with these for now based on a new branch bare_opt that I committed on Friday.
The goal is to build basic optimizer objects which implement a simple mathematical operation, without any built-in assumptions that the user is doing ML, mathematical optimization, or anything really. These optimizers will carry out one step of optimization at a time, while preserving state (learning_rate, momentum, etc). There should be no notion of "data" or "weights". We can then build the higher-level ML focused trainers (with hyperparameters, regularization, etc) on top of these
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Related Issues (20)
- [BUG] Some decompositions/transforms do not preserve derivatives HOT 1
- [BUG] `defer_measurements` does not work with `qml.cond` applied to controlled gates HOT 3
- Define `BasisStateProjector.compute_sparse_matrix` HOT 3
- [BUG] `KerasLayer` has unintended side effects on its QNode HOT 1
- [BUG] Documentation generated does not show the parameters for subclasses of Operation. HOT 1
- Add support for Quantum ESPRESSO HOT 2
- [BUG] `QuantumScript.map_to_standard_wires` sometimes returns original tape HOT 2
- [CHORE] Update `docs/requirements.txt` to reduce package bloat
- [BUG] Differentiation does not work with `dynamic_one_shot` HOT 1
- [BUG] Different ground states when batching with vmap or calling the function directly HOT 5
- Add `PrepSelPrep` block encoding template HOT 4
- Support variable number of trotter steps in the UCCSD ansatz HOT 12
- [BUG] No warnings or errors are being raised when `shots` argument is passed arbitrarily to `@qnode` with no applicable gradient method HOT 5
- [BUG] `qml.simplify` removes information regarding `num_steps` from `Exp`
- [BUG] QAOA layers incorrectly forbid `Sum` observables
- Add function to convert PennyLane objects to OpenFermion objects HOT 2
- [BUG] Adjoint method does not respect global wires return types
- [BUG] TorchLayer does not work correctly with broadcasting and `tuple` returns HOT 1
- Propagate the gradient to part of an array only HOT 2
- Prevent writing to a specific file when creating the Hamiltonian HOT 12
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