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--- ml:quantum_machine_learning [2022/07/08 01:39] – [Overviews] jmflanig
+++ ml:quantum_machine_learning [2025/10/07 06:15] (current) – [Papers] jmflanig
@@ Line 18: / Line 18: @@
     * [[https://arxiv.org/pdf/1405.7479.pdf|Grice & Meyer 2015 - A quantum algorithm for Viterbi decoding of classical convolutional codes]]
     * [[https://faculty.cc.gatech.edu/~bboots3/files/learning_hqmms.pdf|Srinivasan et al 2018 - Learning Hidden Quantum Markov Models]]
+  * Natural Language Processing
+    * [[https://arxiv.org/pdf/2012.03755.pdf|Coecke et al 2020 - Foundations for Near-Term Quantum Natural Language Processing]]
+  * Quantum Transformer Models
+    * [[https://arxiv.org/pdf/2406.04305|Khatri et al 2024 - Quixer: A Quantum Transformer Model]]
   * Quantum Learning Theory
+===== Resources =====
+See Sec 3.3 of [[https://arxiv.org/pdf/2201.04093.pdf|García 2022]].
+  * Hardware
+    * IBM: [[https://quantum-computing.ibm.com/|IBM Quantum]] Access to IBM's experimental quantum computer
+    * [[ https://aws.amazon.com/en/braket/|Amazon Braket]] Access to a number of experimental quantum processing units such as [[https://www.dwavesys.com|D-Wave]], [[https://ionq.com|IonQ]] and [[https://www.rigetti.com/|Rigetti]], as well as three proprietary simulators (SV1, DM1, and TN1).
+  * Software
+    * TensorFlow Quantum (TFQ)