# Batch inputs and TorchLayer interactions with composite quantum circuit **URL:** \ **Category:** PennyLane Help\ **Created:** [September 18, 2025, 5:36pm UTC](https://discuss.pennylane.ai/t/batch-inputs-and-torchlayer-interactions-with-composite-quantum-circuit/9030 "2025-09-18T17:36:59Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![danim](https://avatars.discourse-cdn.com/v4/letter/d/c57346/32.png) [@danim](https://discuss.pennylane.ai/u/danim)\ **Post date:** [September 18, 2025, 5:37pm UTC](https://discuss.pennylane.ai/t/batch-inputs-and-torchlayer-interactions-with-composite-quantum-circuit/9030/1 "2025-09-18T17:37:00Z")
Hello! I’m quite new to both PyTorch and PennyLane, but I’m trying to build a quantum autoencoder, following [this article](https://arxiv.org/html/2505.01875), for didactic purposes. I’ve already written something but the more i debug the more it seems as everything fall apart. I reckoned one major problem of my code would be that i tried to implement the encoder, decoder and other parts of my code without using qml.qnn.TorchLayer, which only then I learned it’s been made for these kind of things. So I’m trying to implement the change, but I have structural doubts. You see, until now i structured my model as a sequence of five classes, namely Preprocessor, Encoder, Compressor, Decoder and Postprocessor, which expand PyTorch’s nn.Module (except the processors). Encoder, Compressor and Decoder are quantum, meaning that they’re made of a variational quantum circuit, with trainable parameters except for the input encoding. As an example, i’ll paste here the code of the encoder (the others are analogous): ```auto class Encoder(nn.Module): def __init__ (self, nQubits, depth): super(). __init__ () self.nQubits = nQubits self.depth = depth self.rotParams = nn.Parameter(torch.rand(depth, nQubits, 3, device=tDevice), requires_grad=True) self.entParams = nn.Parameter(torch.rand(depth, nQubits, device=tDevice), requires_grad=True) def forward(self): self._circuit() def _circuit(self): for i in range(self.depth): self._repeatedLayer(layer=i) def _repeatedLayer(self, layer): # Rotations for i in range(self.nQubits): qml.Rot(self.rotParams[layer,i,0], self.rotParams[layer,i,1], self.rotParams[layer,i,2], wires=i) # Entanglement for i in range(self.nQubits-1): qml.CRZ(self.entParams[layer,i], wires=(i,i+1)) qml.CRZ(self.entParams[layer,self.nQubits-1], wires=(self.nQubits-1,0)) ``` The three quantum circuit would be then incorporated in an upper level class, which would run every circuit and return the results: ```auto class MolQAE(nn.Module): def __init__ (self, nQubits, latQubits, eDepth, dDepth): super(). __init__ () self.nQubits = nQubits self.latQubits = latQubits self.eDepth = eDepth self.dDepth = dDepth self.embedder = StateEmbedder(self.nQubits) self.encoder = Encoder(self.nQubits, self.eDepth) self.compressor = Compressor(self.nQubits, self.latQubits) self.decoder = Decoder(self.nQubits, self.dDepth) self.qnode = qml.QNode(self._circuit, qDevice, interface='torch', diff_method=diffMethod) self.qnode = TorchLayer(self.qnode) def forward(self, input): output = qml.snapshots(self.qnode)(input) initial = output['initialState'] trash = output['trashState'] final = output['execution_results'] return initial, trash, final def _circuit(self, inputs): self.embedder(inputs) qml.Snapshot('initialState', measurement=qml.probs(wires=range(self.nQubits))) self.encoder() self.compressor() qml.Snapshot('trashState', measurement=qml.probs(wires=range(self.nQubits))) self.decoder() return qml.probs(wires=range(self.nQubits)) ``` Here StateEmbedder is a class which handles the input state encoding. So, this is what i’ve done, but as said everything seems to be wrong now that i need to add TorchLayer (i need it because i want proper batch handling and without it many inefficient for loops are to be used, as far as i understand). For instances: - Am i supposed to not wrap into a qnode every circuit in encoder/compressor/decoder, but only the circuit in the upper level class? And if not, how i deal with the fact that i must return a measurement at the end of a node, but i need not? - At which level should i add TorchLayer? I mean, should i wrap Encoder, Compressor and Decoder as TorchLayers or it’s ok wrapping only MolQAE? Since my classes are separate, how should i deal with trainable parameters? I know TorchLayer automatically makes and stores its parameters (right?), but what if i have “nested” circuits? I hope i wrote my doubts clear. Sorry if it’s a lot but it seems i just can’t get my head around it. And thanks to anyone who will answer. P.S.: Anyway, here’s qml.about(): ```auto Name: PennyLane Version: 0.41.1 Summary: PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network. Home-page: https://github.com/PennyLaneAI/pennylane Author: Author-email: License: Apache License 2.0 Location: /home/famiglia/miniconda3/lib/python3.13/site-packages Requires: appdirs, autograd, autoray, cachetools, diastatic-malt, networkx, numpy, packaging, pennylane-lightning, requests, rustworkx, scipy, tomlkit, typing-extensions Required-by: PennyLane_Lightning Platform info: Linux-6.14.0-29-generic-x86_64-with-glibc2.41 Python version: 3.13.2 Numpy version: 2.3.3 Scipy version: 1.16.0 Installed devices: - lightning.qubit (PennyLane_Lightning-0.41.1) - default.clifford (PennyLane-0.41.1) - default.gaussian (PennyLane-0.41.1) - default.mixed (PennyLane-0.41.1) - default.qubit (PennyLane-0.41.1) - default.qutrit (PennyLane-0.41.1) - default.qutrit.mixed (PennyLane-0.41.1) - default.tensor (PennyLane-0.41.1) - null.qubit (PennyLane-0.41.1) - reference.qubit (PennyLane-0.41.1) ``` P.P.S.: I’ll add my python notebook here, for a more comprehensive reference. You’ll find some comments in italian, my language, but don’t worry, it’s nothing important, they’re just annotations. [Notebook download](https://mega.nz/file/JVg0FAIK#IgzFDHGcndfOTDPGAb5lHy7O4uYNJYff5ERpm3pKG-4) ---
**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:** [September 19, 2025, 6:05pm UTC](https://discuss.pennylane.ai/t/batch-inputs-and-torchlayer-interactions-with-composite-quantum-circuit/9030/2 "2025-09-19T18:05:34Z")
Hi @danim , welcome to the Forum! Building a quantum autoencoder is not the easiest project, especially if you’re new to PyTorch and PennyLane. Since you mentioned you’re building this for didactic purposes I would actually recommend going through the [PennyLane Codebook](https://pennylane.ai/codebook) instead. It’s built with learners in mind! If you already know quantum computing (maybe a different SDK) I’d recommend starting with the [PennyLane Fundamentals module](https://pennylane.ai/codebook/pennylane-fundamentals). If you’re new to quantum computing, then you could start with the module on Introduction to Quantum Computing or the [learning path](https://pennylane.ai/codebook/learning-paths) on Fundamentals of Quantum Computing. That being said, if you specifically want to write a quantum autoencoder I would recommend starting from the code in the ANNNI demo. It has a section on [Quantum Anomaly Detection](https://pennylane.ai/qml/demos/tutorial_annni#unsupervised-learning-of-phases-quantum-anomaly-detection), which is the quantum version of an autoencoder. Let us know if you have any further questions! I hope this helps.