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+[Google TensorFlow and IBM Software Stacks](https://drive.google.com/file/d/1gR-JlNDo2WR5kDK_SW5mvgwd8ZNT-rxo/view?usp=share_link) PDF 5/1/23.
+
+Quantum machine learning is showing potential to bring additional power for classical models "that have become more
+sophisticated and expensive to train" over time. Qml software stacks for Google TensorFlow Quantum (TFQ) and IBM Qiskit
+Quantum Machine Learning (QML) illustrate hybrid quantum-classical workflows which incorporate quantum algorithms with
+TensorFlow and Pytorch (see attachments). The two diagrams are applicable to researchers who would like to commit to a qml
+platform for machine learning type projects. <br>
+
+Google's TensorFlow Quantum integrates quantum computing algorithms and logic designed in Google Cirq. Quantum
+computers can then assist machine learning due to their increasing ability to perform fast linear algebra on a state space that
+grows exponentially with the number of qubits. In general, the goals of using TFQ for qml are "to optimize over a parameterized
+class of computations" to generate certain low energy wavefunction, learn to extract non-local information, and learn how to
+generate a quantum distribution from data. <br>
+
+At the top of the TensorFlow Quantum software stack attached begins with either classical data or quantum data. (both data
+types are commonly interconverted, but can result in a reduction from max performance). Classical data is processed by
+TensorFlow, while quantum circuits and quantum operators are processed by TFQ. Next, Keras Models process both classical
+data which proceeds to TF Layers, and quantum data which proceeds to TFQ Layers and TFQ Differentiators. TF Ops and TFQ
+Ops separately instantiatiate dataflow graphs. TFQ qsim quantum simulator processes data by quantum mechanics on a
+classical computer, and Cirq is for creating quantum algorithms. TPUs, GPUs, CPUs, and QPUs are available for utilization at
+the bottom of the stack. <br>