# Circuit execution for batched inputs

**URL:** <https://discuss.pennylane.ai/t/circuit-execution-for-batched-inputs/8548>\
**Category:** PennyLane Help\
**Created:** [May 22, 2025, 3:45pm UTC](https://discuss.pennylane.ai/t/circuit-execution-for-batched-inputs/8548 "2025-05-22T15:45:07Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![CatalinaAlbornoz](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/catalinaalbornoz/32/1196_2.png) [@CatalinaAlbornoz](https://discuss.pennylane.ai/u/CatalinaAlbornoz)\
**Post date:** [May 22, 2025, 9:57pm UTC](https://discuss.pennylane.ai/t/circuit-execution-for-batched-inputs/8548/2 "2025-05-22T21:57:18Z")

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Hi @JesusBG , welcome to the Forum!

`default.qubit` utilizes [NumPy broadcasting](https://numpy.org/doc/stable/user/basics.broadcasting.html) under the hood:

> Broadcasting provides a means of vectorizing array operations so that looping occurs in C instead of Python.

So it makes sense that the benefit of using `default.qubit` with batched inputs scales well.

Since `lightning.qubit` and `lightning.gpu` are already built on C++ I can see why the benefit from batching with those devices is lower.

Feel free to take a look at [Forum post #8403](https://discuss.pennylane.ai/t/is-parameter-broadcasting-considered-parallel-or-concurrent-processing-when-using-pennylane/8403/15), you might find it useful too.

I hope this helps!

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