Fashion-MNIST, CIFAR-10 and Imagenette

content/other/low-depth-image-circuits/cifar-10/citation.txt

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+@misc{kiwit2025efficientdata,
+    title={Low-Depth MNIST}, 
+    author={Florian Kiwit and Bernhard Jobst and Andre Luckow and Frank Pollmann and Carlos Riofrío},
+    howpublished = {\url{https://pennylane.ai/datasets/low-depth-mnist}}
+    year={2025}}

content/other/low-depth-image-circuits/cifar-10/dataset.json

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+{
+  "slug": "low-depth-cifar-10",
+  "class": {
+    "slug": "low-depth-cifar-10",
+    "name": "LowDepthCIFAR10",
+    "attributeList": [
+      {
+        "name": "circuit_layout_d4",
+        "pythonType": "list",
+        "doc": "Circuits layout consiting of CNOTs and RY gates defined as lists of operations and wires they act on with a circuit depth of 4."
+      },
+      {
+        "name": "circuit_layout_d8",
+        "pythonType": "list",
+        "doc": "Circuits layout consiting of CNOTs and RY gates defined as lists of operations and wires they act on with a circuit depth of 8."
+      },
+      {
+        "name": "params_d4",
+        "pythonType": "list",
+        "doc": "Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI)."
+      },
+      {
+        "name": "params_d8",
+        "pythonType": "list",
+        "doc": "Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI)."
+      },
+      {
+        "name": "exact_state",
+        "pythonType": "list[np.ndarray]",
+        "doc": "A list of numpy arrays. Each numpy array defines a quantum state that exactly encodes an CIFAR-10 image."
+      },
+      {
+        "name": "labels",
+        "pythonType": "list",
+        "doc": "A list of the correct labels for each image."
+      }
+    ]
+  },
+  "collection": {
+    "$path": "/other/low-depth-image-circuits/_meta/collection.json"
+  },
+  "data": [
+    {
+      "dataUrl": "https://datasets.cloud.pennylane.ai/user/d9948558-2bea-4c31-99c6-13fa3208ff75",
+      "parameters": {
+        "name": "low-depth-cifar-10"
+      },
+      "extra": {}
+    }
+  ],
+  "downloadName": "low-depth-cifar-10",
+  "features": [
+    {
+      "slug": "dataset-attributes",
+      "title": "Dataset Attributes",
+      "type": "DATA",
+      "content": {
+        "$path": "features/dataset-attributes.md"
+      }
+    }
+  ],
+  "meta": {
+    "$path": "meta.json"
+  },
+  "extra": {
+    "defaultParameters": {
+      "name": "low-depth-cifar-10"
+    }
+  }
+}

content/other/low-depth-image-circuits/cifar-10/features/dataset-attributes.md

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+|Name|Type|Description|
+|-|-|-|
+|`circuit_layout_d4`|`list`|Circuits layout defined as lists of operations and wires they act on with a circuit depth of 4.|
+|`circuit_layout_d8`|`list`|Circuits layout defined as lists of operations and wires they act on with a circuit depth of 8.|
+|`params_d4`|`list`|Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (MCRQI).|
+|`params_d8`|`list`|Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (MCRQI).|
+|`labels`|`list`|A list of the correct labels for each image.|
+|`exact_state`|`list`|A list of numpy arrays. Each numpy array defines an MCRQI state that exactly encodes a CIFAR-10 image.|

content/other/low-depth-image-circuits/cifar-10/meta.json

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+{
+  "abstract": null,
+  "authors": [
+    {
+      "name": "Florian Kiwit",
+      "username": "flokiwit"
+    },
+    {
+      "name": "Bernhard Jobst"
+    },
+    {
+      "name": "Andre Luckow"
+    },
+    {
+      "name": "Frank Pollmann"
+    },
+    {
+      "name": "Carlos Riofrío"
+    }
+  ],
+  "based_on_papers": true,
+  "citation": {
+    "$path": "citation.txt"
+  },
+  "changelog": [
+    "version 0.1 : initial public release"
+  ],
+  "license": "[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
+  "sourceCodeUrl": null,
+  "tags": [
+    "Other"
+  ],
+  "title": "Low-Depth CIFAR-10",
+  "description": "This dataset contains images from the CIFAR-10 dataset encoded as quantum states.",
+  "usingThisDataset": {
+    "$path": "using_this_dataset.md"
+  },
+  "heroImage": "https://assets.cloud.pennylane.ai/datasets/generic/hero/Datasets_GenericHero_2.png",
+  "thumbnail": "Dataset_CIFAR10_thumb.png",
+  "extra": {},
+  "dateOfLastModification": "2025-04-23",
+  "dateOfPublication": "2025-04-23"
+}

content/other/low-depth-image-circuits/cifar-10/using_this_dataset.md

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+Data for benchmarking machine learning models, generated for an upcoming paper: *Typical Machine Learning Datasets as Low-Depth Quantum Circuits*.
+
+**Description of the dataset**
+
+The [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) contains 60,000 32×32 color images across 10 categories (e.g., airplanes, cars, birds, and cats), with 6,000 images per category. Here, we provide circuit parameters that approximate the [Multi-Channel Representation of Quantum Images (MCRQI)](https://ieeexplore.ieee.org/document/6051718) of each image in the CIFAR-10 dataset.
+
+**Additional details**
+
+- The class labels are integers from 0 to 9.
+- Implementing the circuits in this dataset and obtaining the final state with PennyLane's `qml.state()` outputs a state vector. This state vector must be processed to recover the original image.
+- The dataset contains two circuits per image: those with a depth of four, which are shallower, and those with a depth of eight, which provide more accurate approximations of the exact state.
+- The `exact_state` entry contains a list of numpy arrays representing MCRQI states that exactly encode Imagenette images. This significantly increases the file size and can be omitted during download if not needed.
+
+**Example usage**
+
+```python
+import pennylane as qml
+import jax
+
+[dataset_params] = qml.data.load("low-depth-cifar-10")
+
+def get_circuit(circuit_layout):
+    dev = qml.device("default.qubit", wires=13)
+    @jax.jit
+    @qml.qnode(dev)
+    def circuit(params):
+        counter = 0
+        for gate, wire in circuit_layout:
+
+            if gate == "RY":
+                qml.RY(params[counter], wire)
+                counter += 1
+
+            elif gate == "CNOT":
+                qml.CNOT(wire)
+
+        return qml.state()
+
+    return circuit
+
+# Example for running the circuit with depth 4
+circuit_layout_d4 = dataset_params.circuit_layout_d4
+circuit_d4 = get_circuit(circuit_layout_d4)
+state_d4 = circuit_d4(dataset_params.params_d4[0])
+
+# Example for running the circuit with depth 8
+circuit_layout_d8 = dataset_params.circuit_layout_d8
+circuit_d8 = get_circuit(circuit_layout_d8)
+state_d8 = circuit_d8(dataset_params.params_d8[0])
+```

content/other/low-depth-image-circuits/fashion-mnist/citation.txt

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+@misc{kiwit2025efficientdata,
+    title={Low-Depth MNIST}, 
+    author={Florian Kiwit and Bernhard Jobst and Andre Luckow and Frank Pollmann and Carlos Riofrío},
+    howpublished = {\url{https://pennylane.ai/datasets/low-depth-mnist}}
+    year={2025}}

content/other/low-depth-image-circuits/fashion-mnist/dataset.json

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+{
+  "slug": "low-depth-fashion-mnist",
+  "class": {
+    "slug": "low-depth-fashion-mnist",
+    "name": "LowDepthFashionMNIST",
+    "attributeList": [
+      {
+        "name": "circuit_layout_d4",
+        "pythonType": "list",
+        "doc": "Circuits layout consiting of CNOTs and RY gates defined as lists of operations and wires they act on with a circuit depth of 4."
+      },
+      {
+        "name": "circuit_layout_d8",
+        "pythonType": "list",
+        "doc": "Circuits layout consiting of CNOTs and RY gates defined as lists of operations and wires they act on with a circuit depth of 8."
+      },
+      {
+        "name": "params_d4",
+        "pythonType": "list",
+        "doc": "Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI)."
+      },
+      {
+        "name": "params_d8",
+        "pythonType": "list",
+        "doc": "Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI)."
+      },
+      {
+        "name": "exact_state",
+        "pythonType": "list[np.ndarray]",
+        "doc": "A list of numpy arrays. Each numpy array defines a quantum state that exactly encodes an Fashion-MNIST image."
+      },
+      {
+        "name": "labels",
+        "pythonType": "list",
+        "doc": "A list of the correct labels for each image."
+      }
+    ]
+  },
+  "collection": {
+    "$path": "/other/low-depth-image-circuits/_meta/collection.json"
+  },
+  "data": [
+    {
+      "dataUrl": "https://datasets.cloud.pennylane.ai/user/54d8b630-61c9-4e32-a0a5-2aceac9b278f",
+      "parameters": {
+        "name": "low-depth-fashion-mnist"
+      },
+      "extra": {}
+    }
+  ],
+  "downloadName": "low-depth-fashion-mnist",
+  "features": [
+    {
+      "slug": "dataset-attributes",
+      "title": "Dataset Attributes",
+      "type": "DATA",
+      "content": {
+        "$path": "features/dataset-attributes.md"
+      }
+    }
+  ],
+  "meta": {
+    "$path": "meta.json"
+  },
+  "extra": {
+    "defaultParameters": {
+      "name": "low-depth-fashion-mnist"
+    }
+  }
+}

content/other/low-depth-image-circuits/fashion-mnist/features/dataset-attributes.md

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+|Name|Type|Description|
+|-|-|-|
+|`circuit_layout_d4`|`list`|Circuits layout defined as lists of operations and wires they act on with a circuit depth of 4.|
+|`circuit_layout_d8`|`list`|Circuits layout defined as lists of operations and wires they act on with a circuit depth of 8.|
+|`params_d4`|`list`|Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI).|
+|`params_d8`|`list`|Circuit parameters as list of RY angles. When plugged in to the circuit layout with a depth of 8, the circuit approximates the flexible representation of quantum images (FRQI).|
+|`labels`|`list`|A list of the correct labels for each image.|
+|`exact_state`|`list`|A list of numpy arrays. Each numpy array defines an FRQI state that exactly encodes a Fashion-MNIST image.|

content/other/low-depth-image-circuits/fashion-mnist/meta.json

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+{
+  "abstract": null,
+  "authors": [
+    {
+      "name": "Florian Kiwit",
+      "username": "flokiwit"
+    },
+    {
+      "name": "Bernhard Jobst"
+    },
+    {
+      "name": "Andre Luckow"
+    },
+    {
+      "name": "Frank Pollmann"
+    },
+    {
+      "name": "Carlos Riofrío"
+    }
+  ],
+  "based_on_papers": true,
+  "citation": {
+    "$path": "citation.txt"
+  },
+  "changelog": [
+    "version 0.1 : initial public release"
+  ],
+  "license": "[CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en)",
+  "sourceCodeUrl": null,
+  "tags": [
+    "Other"
+  ],
+  "title": "Low-Depth Fashion-MNIST",
+  "description": "This dataset contains images from the Fashion-MNIST dataset encoded as quantum states.",
+  "usingThisDataset": {
+    "$path": "using_this_dataset.md"
+  },
+  "heroImage": "https://assets.cloud.pennylane.ai/datasets/generic/hero/Datasets_GenericHero_2.png",
+  "thumbnail": "Dataset_FashionMNIST_thumb.png",
+  "extra": {},
+  "dateOfLastModification": "2025-04-23",
+  "dateOfPublication": "2025-04-23"
+}

content/other/low-depth-image-circuits/fashion-mnist/using_this_dataset.md

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+Data for benchmarking machine learning models, generated for an upcoming paper: *Typical Machine Learning Datasets as Low-Depth Quantum Circuits*.
+
+**Description of the dataset**
+
+The [Fashion-MNIST dataset](https://arxiv.org/abs/1708.07747) has 28x28 grayscale images of 70,000 fashion items across 10 categories (e.g., T-shirts, trousers, shoes), with 7,000 images per category. Here, we provide circuit parameters that approximate the [Flexible Representation of Quantum Images (FRQI)](https://link.springer.com/article/10.1007/s11128-010-0177-y) of each image in the Fashion-MNIST dataset.
+
+**Additional details**
+
+- The class labels are integers from 0 to 9.
+- Implementing the circuits in this dataset and obtaining the final state with PennyLane's `qml.state()` outputs a state vector. This state vector must be processed to recover the original image.
+- The dataset contains two circuits per image: those with a depth of four, which are shallower, and those with a depth of eight, which provide more accurate approximations of the exact state.
+- The `exact_state` entry contains a list of numpy arrays representing FRQI states that exactly encode Imagenette images. This significantly increases the file size and can be omitted during download if not needed.
+
+**Example usage**
+
+```python
+import pennylane as qml
+import jax
+
+[dataset_params] = qml.data.load("low-depth-fashion-mnist")
+
+def get_circuit(circuit_layout):
+    dev = qml.device("default.qubit", wires=11)
+    @jax.jit
+    @qml.qnode(dev)
+    def circuit(params):
+        counter = 0
+        for gate, wire in circuit_layout:
+
+            if gate == "RY":
+                qml.RY(params[counter], wire)
+                counter += 1
+
+            elif gate == "CNOT":
+                qml.CNOT(wire)
+
+        return qml.state()
+
+    return circuit
+
+# Example for running the circuit with depth 4
+circuit_layout_d4 = dataset_params.circuit_layout_d4
+circuit_d4 = get_circuit(circuit_layout_d4)
+state_d4 = circuit_d4(dataset_params.params_d4[0])
+
+# Example for running the circuit with depth 8
+circuit_layout_d8 = dataset_params.circuit_layout_d8
+circuit_d8 = get_circuit(circuit_layout_d8)
+state_d8 = circuit_d8(dataset_params.params_d8[0])
+```

content/other/low-depth-image-circuits/imagenette/citation.txt

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+@misc{kiwit2025efficientdata,
+    title={Low-Depth MNIST}, 
+    author={Florian Kiwit and Bernhard Jobst and Andre Luckow and Frank Pollmann and Carlos Riofrío},
+    howpublished = {\url{https://pennylane.ai/datasets/low-depth-mnist}}
+    year={2025}}