docs(block-encoding): fix broken_link

Author: virajd98

applications/block_encoding/select_structures_BE.ipynb

@@ -8,7 +8,7 @@
     "# Block encoding of select structured matrices\n",
     "___\n",
     "\n",
-    "This work is an implementation of the paper on [Block-encoding structured matrices for data input in quantum computing](https://quantum-journal.org/papers/q-2024-01-11-1226/)."
+    "This work is an implementation of the paper on **Block-encoding structured matrices for data input in quantum computing** (https://quantum-journal.org/papers/q-2024-01-11-1226/)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     "\n",
     "where,  $A = \\alpha \\tilde{A}$ . The parameters $(\\alpha, a)$  represent the *subnormalization factor* (which adjusts for encoding matrices of any norm), and the *number of ancilla qubits* used in the block-encoding scheme respectively. \n",
     "\n",
-    "Efficiently block encoding arbitrary matrices is a very difficult problem and this task is not trivial even for well structured and sparse matrices. The [paper by Sunderhauf et al. 2024](https://quantum-journal.org/papers/q-2024-01-11-1226/), provides for efficient quantum circuits for block encoding arithmetically structured matrices. This is useful in many applications, especially ones involving problems of linear algebra. Moreover, given an efficient block encoding of a matrix $\\tilde{A}$, its possible to efficiently construct a block encoding of certain polynomials of $\\tilde{A}$ through quantum singular value transformations (QSVT). \n",
+    "Efficiently block encoding arbitrary matrices is a very difficult problem and this task is not trivial even for well structured and sparse matrices. The paper by Sunderhauf et al. 2024 (https://quantum-journal.org/papers/q-2024-01-11-1226/), provides for efficient quantum circuits for block encoding arithmetically structured matrices. This is useful in many applications, especially ones involving problems of linear algebra. Moreover, given an efficient block encoding of a matrix $\\tilde{A}$, its possible to efficiently construct a block encoding of certain polynomials of $\\tilde{A}$ through quantum singular value transformations (QSVT). \n",
     "\n",
     "### **Notebook contents**\n",
     "- ##### Block encoding circuits for Checkerboard matrix, Toeplitz matrix, Tridiagonal symmetric matrix and 2D Laplacian matrix.\n",