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PennyLaneAI · Sep 20, 2024 · fb0658d · fb0658d
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diff --git a/_static/authors/joseph_bunao.jpg b/_static/authors/joseph_bunao.jpg
diff --git a/_static/authors/joseph_bunao.txt b/_static/authors/joseph_bunao.txt
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+.. bio:: Joseph Bunao
+   :photo: ../_static/authors/joseph_bunao.jpg
+
+   Joseph is a machine learning specialist at Xanadu. His main focus is to accelerate quantum hardware research and processes using machine learning.
diff --git a/...bnails/large_demo_thumbnails/thumbnail_large_generative_quantum_eigensolver.png b/...bnails/large_demo_thumbnails/thumbnail_large_generative_quantum_eigensolver.png
diff --git a/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_mps_simulation.png b/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_mps_simulation.png
diff --git a/...bnails/opengraph_demo_thumbnails/OGthumbnail_generative_quantum_eigensolver.png b/...bnails/opengraph_demo_thumbnails/OGthumbnail_generative_quantum_eigensolver.png
diff --git a/_static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_mps_simulation.png b/_static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_mps_simulation.png
diff --git a/...thumbnails/regular_demo_thumbnails/thumbnail_generative_quantum_eigensolver.png b/...thumbnails/regular_demo_thumbnails/thumbnail_generative_quantum_eigensolver.png
diff --git a/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_mps_simulation.png b/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_mps_simulation.png
diff --git a/_static/demonstration_assets/gqe_training/gqe_performance.png b/_static/demonstration_assets/gqe_training/gqe_performance.png
diff --git a/_static/demonstration_assets/gqe_training/gqe_training_diagram.png b/_static/demonstration_assets/gqe_training/gqe_training_diagram.png
diff --git a/_static/demonstration_assets/gqe_training/gqe_training_loss.png b/_static/demonstration_assets/gqe_training/gqe_training_loss.png
diff --git a/_static/demonstration_assets/gqe_training/paper_gqe_vqe.png b/_static/demonstration_assets/gqe_training/paper_gqe_vqe.png
diff --git a/_static/demonstration_assets/mps/apply_gate.png b/_static/demonstration_assets/mps/apply_gate.png
diff --git a/_static/demonstration_assets/mps/area_law.png b/_static/demonstration_assets/mps/area_law.png
diff --git a/_static/demonstration_assets/mps/cnot_split.png b/_static/demonstration_assets/mps/cnot_split.png
diff --git a/_static/demonstration_assets/mps/final_expval.png b/_static/demonstration_assets/mps/final_expval.png
diff --git a/_static/demonstration_assets/mps/left_orthonormal.png b/_static/demonstration_assets/mps/left_orthonormal.png
diff --git a/_static/demonstration_assets/mps/mixed_canonical_observable.png b/_static/demonstration_assets/mps/mixed_canonical_observable.png
diff --git a/_static/demonstration_assets/mps/non_local_cnot.png b/_static/demonstration_assets/mps/non_local_cnot.png
diff --git a/_static/demonstration_assets/mps/norm_trivial.png b/_static/demonstration_assets/mps/norm_trivial.png
diff --git a/_static/demonstration_assets/mps/psi_to_mps_0.png b/_static/demonstration_assets/mps/psi_to_mps_0.png
diff --git a/_static/demonstration_assets/mps/psi_to_mps_1.png b/_static/demonstration_assets/mps/psi_to_mps_1.png
diff --git a/_static/demonstration_assets/mps/psi_to_mps_2.png b/_static/demonstration_assets/mps/psi_to_mps_2.png
diff --git a/_static/demonstration_assets/mps/psi_to_mps_3.png b/_static/demonstration_assets/mps/psi_to_mps_3.png
diff --git a/_static/demonstration_assets/mps/vidal.png b/_static/demonstration_assets/mps/vidal.png
diff --git a/demonstrations/gqe_training.metadata.json b/demonstrations/gqe_training.metadata.json
@@ -0,0 +1,74 @@
+{
+    "title": "Generative quantum eigensolver training using PennyLane data",
+    "authors": [
+        {
+            "username": "Joseph"
+        }, 
+        {
+            "username": "zy_n"
+        }
+    ],
+    "dateOfPublication": "2024-09-20T00:00:00+00:00",
+    "dateOfLastModification": "2024-09-20T00:00:00+00:00",
+    "categories": ["Quantum Machine Learning", "Quantum Chemistry", "Algorithms"],
+    "tags": [],
+    "previewImages": [
+        {
+            "type": "thumbnail",
+            "uri": "_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_generative_quantum_eigensolver.png"
+        },
+        {
+            "type": "large_thumbnail",
+            "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_generative_quantum_eigensolver.png"
+        }
+    ],
+    "seoDescription": "Learn how you can train a small GPT model using the generative quantum eigensolver (GQE) technique and PennyLane data.",
+    "doi": "",
+    "canonicalURL": "/qml/demos/gqe_training",
+    "references": [
+        {
+            "id": "nakaji2024",
+            "type": "article",
+            "title": "The generative quantum eigensolver (GQE) and its application for ground state search",
+            "authors": "K. Nakaji, L. B. Kristensen, J. A. Campos-Gonzalez-Angulo, M. G. Vakili, H. Huang, M. Bagherimehrab, C. Gorgulla, F. Wong, A. McCaskey, J. S. Kim, T. Nguyen, P. Rao, A. Aspuru-Guzik",
+            "year": "2024",
+            "journal": "",
+            "url": "https://arxiv.org/abs/2401.09253"
+        },
+        {
+            "id": "radford2019",
+            "type": "article",
+            "title": "Language Models are Unsupervised Multitask Learners",
+            "authors": "A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever",
+            "year": "2019",
+            "journal": "OpenAI blog",
+            "url": "https://openai.com/index/better-language-models/"
+        },
+        {
+            "id": "vaswani2017",
+            "type": "article",
+            "title": "Attention is All you Need",
+            "authors": "A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin",
+            "year": "2017",
+            "journal": "Advances in Neural Information Processing Systems",
+            "url": "https://papers.nips.cc/paper_files/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"
+        }
+    ],
+    "basedOnPapers": [
+        "10.48550/arXiv.2401.09253"
+    ],
+    "referencedByPapers": [],
+    "relatedContent": [
+        {
+            "type": "demonstration",
+            "id": "tutorial_vqe",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "tutorial_vqe_qng",
+            "weight": 1.0
+        }
+    ],
+    "hardware": []
+}
diff --git a/demonstrations/gqe_training.py b/demonstrations/gqe_training.py
diff --git a/demonstrations/tutorial_mps.metadata.json b/demonstrations/tutorial_mps.metadata.json
@@ -0,0 +1,100 @@
+{
+    "title": "Introducing matrix product states for quantum practitioners",
+    "authors": [
+        {
+            "username": "Qottmann"
+        }
+    ],
+    "dateOfPublication": "2024-09-29T00:00:00+00:00",
+    "dateOfLastModification": "2024-09-29T00:00:00+00:00",
+    "categories": [
+        "Quantum Computing",
+        "Algorithms",
+        "Getting Started"
+    ],
+    "tags": [],
+    "previewImages": [
+        {
+            "type": "thumbnail",
+            "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_mps_simulation.png"
+        },
+        {
+            "type": "large_thumbnail",
+            "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_mps_simulation.png"
+        }
+    ],
+    "seoDescription": "What is a matrix product state (MPS)? Here is all you need to know about MPS from a quantum computation and quantum simulation perspective.",
+    "doi": "",
+    "canonicalURL": "/qml/demos/tutorial_mps",
+    "references": [
+        {
+            "id": "Baiardi",
+            "type": "preprint",
+            "title": "The Density Matrix Renormalization Group in Chemistry and Molecular Physics: Recent Developments and New Challenges",
+            "authors": "Alberto Baiardi, Markus Reiher",
+            "year": "2019",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.1910.00137",
+            "url": "https://arxiv.org/abs/1910.00137"
+        },
+        {
+            "id": "Patra",
+            "type": "preprint",
+            "title": "Efficient tensor network simulation of IBM's largest quantum processors",
+            "authors": "Siddhartha Patra, Saeed S. Jahromi, Sukhbinder Singh, Roman Orus",
+            "year": "2023",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.2309.15642",
+            "url": "https://arxiv.org/abs/2309.15642"
+        },
+        {
+            "id": "Jiang",
+            "type": "preprint",
+            "title": "Accurate determination of tensor network state of quantum lattice models in two dimensions",
+            "authors": "H. C. Jiang, Z. Y. Weng, T. Xiang",
+            "year": "2008",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.0806.3719",
+            "url": "https://arxiv.org/abs/0806.3719"
+        },
+        {
+            "id": "Vidal",
+            "type": "preprint",
+            "title": "Efficient classical simulation of slightly entangled quantum computations",
+            "authors": "Guifre Vidal",
+            "year": "2003",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.quant-ph/0301063",
+            "url": "https://arxiv.org/abs/quant-ph/0301063"
+        },
+        {
+            "id": "Schollwoeck",
+            "type": "preprint",
+            "title": "The density-matrix renormalization group in the age of Matrix Product States",
+            "authors": "Ulrich Schollwoeck",
+            "year": "2010",
+            "publisher": "",
+            "journal": "",
+            "doi": "10.48550/arXiv.1008.3477",
+            "url": "https://arxiv.org/abs/1008.3477"
+        }
+    ],
+    "basedOnPapers": ["10.48550/arXiv.1008.3477"],
+    "referencedByPapers": [],
+    "relatedContent": [
+        {
+            "type": "demonstration",
+            "id": "tutorial_How_to_simulate_quantum_circuits_with_tensor_networks",
+            "weight": 1.0
+        },
+        {
+            "type": "demonstration",
+            "id": "tutorial_diffable-mitigation",
+            "weight": 1.0
+        }
+    ]
+}