update collection thumbnails

Author: ashishks0522

content/other/hamlib/_meta/collection.json

@@ -2,5 +2,5 @@
   "slug": "hamlib",
   "title": "HamLib",
   "about": "This collection contains portions of [HamLib](https://arxiv.org/abs/2306.13126): a library of Hamiltonians for benchmarking quantum algorithms and hardware. We currently support Hamiltonians for several optimization problems.",
-  "thumbnail": "DatasetCollections_HamLib_Thumb.png"
+  "thumbnail": "pennylane-datasets-collection-hamlib.png"
 }

content/other/hamlib/_meta/DatasetCollections_HamLib_Thumb.png renamed to content/other/hamlib/_meta/pennylane-datasets-collection-hamlib.png

@@ -2,5 +2,5 @@
   "slug": "low-depth-image-circuits",
   "title": "Low-Depth Image Circuits",
   "about": "This collection contains datasets from an upcoming paper, Typical Machine Learning Datasets as Low-Depth Quantum Circuits. These datasets contain quantum circuits that encode images as quantum states and can be used for benchmarking supervised quantum machine learning models.",
-  "thumbnail": "DatasetCollections_QuantumImages.png"
+  "thumbnail": "pennylane-datasets-collection-low-depth-image-circuits.png"
 }

content/other/low-depth-image-circuits/_meta/collection.json

@@ -2,5 +2,5 @@
   "slug": "phase-angles",
   "title": "QSVT Phase Angles",
   "about": "This collection contains sets of phase angles to implement polynomials via QSVT, QSP, and GQSP for several functions.",
-  "thumbnail": "DatasetCollections_Angles.png"
+  "thumbnail": "pennylane-datasets-collection-phase-angles.png"
 }

content/other/low-depth-image-circuits/_meta/DatasetCollections_QuantumImages.png renamed to content/other/low-depth-image-circuits/_meta/pennylane-datasets-collection-low-depth-image-circuits.png

@@ -2,5 +2,5 @@
   "slug": "qml-benchmarks",
   "title": "QML Benchmarks",
   "about": "This collection contains datasets from [Better than classical? The subtle art of benchmarking quantum machine learning models](https://arxiv.org/abs/2403.07059) and [Train on classical, deploy on quantum: scaling generative quantum machine learning to a thousand qubits](https://arxiv.org/abs/2503.02934). These datasets offer several classification tasks that can be used for benchmarking quantum machine learning models.",
-  "thumbnail": "DatasetCollections_Benchmarks_Thumb.png"
+  "thumbnail": "pennylane-datasets-collection-qml-benchmarks.png"
 }

content/other/phase-angles/_meta/collection.json

@@ -2,5 +2,5 @@
   "slug": "qchem",
   "title": "PennyLane Molecules",
   "about": "This collection includes quantum properties for various molecules with different basis states and bond lengths. For each molecule, we provide descriptive information, including the Hamiltonian, and calculated values, such as the ground state energy.",
-  "thumbnail": "DatasetCollections_QChem_Thumb.png"
+  "thumbnail": "pennylane-datasets-collection-qchem.png"
 }

content/other/phase-angles/_meta/DatasetCollections_Angles.png renamed to content/other/phase-angles/_meta/pennylane-datasets-collection-phase-angles.png

@@ -2,5 +2,5 @@
   "slug": "qspin",
   "title": "PennyLane Spin Systems",
   "about": "This collection includes quantum properties for various spin systems. For varying onsite interaction values, we provide descriptive information, including the system Hamiltonian, and calculated values, such as measurements of an order parameter.",
-  "thumbnail": "pennylane-datasets-thumbnail-_meta.png"
+  "thumbnail": "pennylane-datasets-collection-qspin.png"
 }