# Batching both parameters and input

**URL:** <https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890>\
**Category:** PennyLane Help\
**Created:** [January 13, 2024, 9:17pm UTC](https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890 "2024-01-13T21:17:27Z")\
**Posts on this page:** 4\
**Page:** 1

<div class="post-metadata">

**Author:** ![Andrea](https://avatars.discourse-cdn.com/v4/letter/a/c37758/32.png) [@Andrea](https://discuss.pennylane.ai/u/Andrea)\
**Post date:** [January 13, 2024, 9:17pm UTC](https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890/1 "2024-01-13T21:17:28Z")

</div>

Hi, with PennyLane we can perform a circuit forward pass with a batch of input states with shape `(batch_size, 2**number_of_qubits)`.

I am trying to batch parmeters as well, using `@qml.batch_params`, I need the parameters batch to correspond to the states batch (first state processed with first parameters, and so on). Right now I am using `StronglyEntanglingLayer`, hence parameters have shape `(batch_size, number_of_layers, number_of_qubits, 3)`.

It seems this doesn’t work, as the output state has a new batch dimension (output shape is `(batch_size, batch_size, 2**number_of_qubits)`.

Am I doing something wrong or this is not intended to work this way? Thanks!

Script below to reproduce the issue.

```auto
import torch
import pennylane as qml

number_of_layers = 3
number_of_qubits = 3
batch_size = 2

@qml.batch_params
@qml.qnode(Sdev, interface="torch")
def circuit(params, state):

    # load initial state
    qml.QubitStateVector(state, wires=range(number_of_qubits))

    # apply circuit
    qml.StronglyEntanglingLayers(params, wires=range(number_of_qubits), ranges = [1]*params.shape[-3])

    # return state vector
    return qml.state()

# define a batch of random states and normalise
state = torch.rand(batch_size, 2**number_of_qubits)
state = state/torch.linalg.vector_norm(state, dim = -1).view(-1, 1)

# define a batch of trainable parameters
S1params = torch.rand(batch_size, number_of_layers, number_of_qubits, 3).requires_grad_()

# apply circuit
output = circuit(S1params, state)

print(output.shape)

```

---

<div class="post-metadata">

**Author:** ![isaacdevlugt](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/isaacdevlugt/32/1159_2.png) [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)\
**Post date:** [January 17, 2024, 12:45am UTC](https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890/2 "2024-01-17T00:45:27Z")

</div>

Hey @Andrea! Welcome to the forum 🚀

If I’m understanding what you want your desired output to be, then I don’t think `qml.batch_params` is necessary:

```auto
import torch
import pennylane as qml

number_of_layers = 3
number_of_qubits = 3
batch_size = 2

dev = qml.device("default.qubit.legacy", wires=number_of_qubits)

@qml.qnode(dev)
def circuit(params, state):

    # load initial state
    qml.QubitStateVector(state, wires=range(number_of_qubits))

    # apply circuit
    qml.StronglyEntanglingLayers(params, wires=range(number_of_qubits), ranges = [1]*params.shape[-3])

    # return state vector
    return qml.state()

# define a batch of random states and normalise
state = torch.rand(batch_size, 2**number_of_qubits)
state = state/torch.linalg.vector_norm(state, dim = -1).view(-1, 1)

# define a batch of trainable parameters
S1params = torch.rand(batch_size, number_of_layers, number_of_qubits, 3).requires_grad_()

# apply circuit
output = circuit(S1params, state)

print(output.shape)
print(dev.num_executions)

```

```auto
torch.Size([2, 8])
1

```

Let me know if this helps!

---

<div class="post-metadata">

**Author:** ![Andrea](https://avatars.discourse-cdn.com/v4/letter/a/c37758/32.png) [@Andrea](https://discuss.pennylane.ai/u/Andrea)\
**Post date:** [January 17, 2024, 2:46pm UTC](https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890/3 "2024-01-17T14:46:04Z")

</div>

Thank you very much, this is exactly what I needed. However if I increase the batch size (e.g. 32), I get the following error

```auto
Traceback (most recent call last):
  File "/raid/home/username/ConditionalDiffusionTimeEmbedding/Modules/pennylane_functions.py", line 90, in <module>
    output = circuit(S1params, state)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/qnode.py", line 1039, in __call__
    res = qml.execute(
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/interfaces/execution.py", line 648, in execute
    results = inner_execute(tapes)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/interfaces/execution.py", line 258, in inner_execute
    tapes = tuple(expand_fn(t) for t in tapes)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/interfaces/execution.py", line 258, in <genexpr>
    tapes = tuple(expand_fn(t) for t in tapes)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/interfaces/execution.py", line 218, in device_expansion_function
    return device.expand_fn(tape, max_expansion=max_expansion)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/_device.py", line 718, in expand_fn
    return self.default_expand_fn(circuit, max_expansion=max_expansion)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/_device.py", line 689, in default_expand_fn
    circuit = _local_tape_expand(
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/_device.py", line 85, in _local_tape_expand
    obj = QuantumScript(obj.decomposition(), _update=False)
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/operation.py", line 1261, in decomposition
    return self.compute_decomposition(
  File "/raid/home/username/.local/lib/python3.10/site-packages/pennylane/templates/layers/strongly_entangling.py", line 210, in compute_decomposition
    weights[..., l, i, 0],
IndexError: index 3 is out of bounds for dimension 1 with size 3

```

---

<div class="post-metadata">

**Author:** ![isaacdevlugt](https://yyz2.discourse-cdn.com/flex012/user_avatar/discuss.pennylane.ai/isaacdevlugt/32/1159_2.png) [@isaacdevlugt](https://discuss.pennylane.ai/u/isaacdevlugt)\
**Post date:** [January 18, 2024, 10:36pm UTC](https://discuss.pennylane.ai/t/batching-both-parameters-and-input/3890/4 "2024-01-18T22:36:33Z")

</div>

Ah! Maybe I’m missing something, but you’re passing one too many dimensions for the parameters for your `StronglyEntanglingLayer` operator 🙂. This should be the parameters’ shape: `(batch_size, num_layers, num_qubits)`.

```auto
import torch
import pennylane as qml

number_of_layers = 3
number_of_qubits = 3
batch_size = 10

dev = qml.device("default.qubit", wires=number_of_qubits)

@qml.qnode(dev)
def circuit(params, state):

    # load initial state
    qml.QubitStateVector(state, wires=range(number_of_qubits))

    # apply circuit
    qml.StronglyEntanglingLayers(params, wires=range(number_of_qubits), ranges = [1]*params.shape[-3])

    # return state vector
    return qml.state()

# define a batch of random states and normalise
state = torch.rand(batch_size, 2**number_of_qubits)
state = state/torch.linalg.vector_norm(state, dim = -1).view(-1, 1)

# define a batch of trainable parameters
S1params = torch.rand(batch_size, number_of_layers, number_of_qubits).requires_grad_()

# apply circuit
with qml.Tracker(dev) as t:
    output = circuit(S1params, state)

print(output.shape)
print(t.latest)

```

```auto
torch.Size([10, 8])
{'simulations': 1, ...

```

Note: In my previous post I used `default.qubit.legacy`, which is our old device API. Our new device API (accessible by using `default.qubit`) also supports broadcasting. The `'simulations'` value shows the number of times the device ran something (once, meaning broadcasting happened 🙂).

Let me know if this helps!