feat: The rigetti.qvm and rigetti.qpu device now support parallel processing.
#148
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## master #148 +/- ##
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| * The `rigetti.qvm` and `rigetti.qpu` device can now be initialized | ||
| with a `parallel` and `max_threads` parameter. When `parallel` is | ||
| set to True, jobs will be executed in parallel using a `ThreadPool`. | ||
| This can be used in conjunction with `max_threads` to set the | ||
| maximum number of worker threads to use. |
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| * The `rigetti.qvm` and `rigetti.qpu` device can now be initialized | |
| with a `parallel` and `max_threads` parameter. When `parallel` is | |
| set to True, jobs will be executed in parallel using a `ThreadPool`. | |
| This can be used in conjunction with `max_threads` to set the | |
| maximum number of worker threads to use. | |
| * The `rigetti.qvm` and `rigetti.qpu` device can now be initialized | |
| with a `parallel` and `max_threads` parameter. When `parallel` is | |
| set to True, jobs will be executed in parallel using a `ThreadPool`. | |
| This can be used in conjunction with `max_threads` to set the | |
| maximum number of worker threads to use. | |
| [(#146)](https://github.com/PennyLaneAI/pennylane-rigetti/pull/148) |
| self._parameter_reference_map[parameter_string] = current_ref | ||
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| # Store the values bound to the symbolic parameter | ||
| self._batched_parameter_map[parameter_string] = operation.data[0] |
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What if it's a Rot gate? They might have more than one parameter. Maybe just only allow parameters with one parameter here?
| par.append(self._parameter_reference_map[parameter_string]) | ||
| else: | ||
| for param in operation.data: | ||
| if getattr(param, "requires_grad", False) and operation.name != "BasisState": |
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Unless this is a "backprop" device, all parameters sent to the device will be purely numpy, not autograd. So I'm trying to figure out whether or not this block would ever be True in a full pennylane workflow, or just when someone is using the device in isolation.
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Hi @MarquessV! Let us know if there is anything we can help with on this. |
This adds support for parallel processing batches, which can greatly improve performance.
[sc-54134]