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docs/tutorials/07_pegasos_qsvc.ipynb

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    "source": [
     "# Pegasos Quantum Support Vector Classifier\n",
     "\n",
-    "There's another SVM based algorithm that benefits from the quantum kernel method. Here, we introduce an implementation of a another classification algorithm, which is an alternative version to the `QSVC` available in Qiskit Machine Learning. This classification algorithm implements the Pegasos algorithm from the paper \"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM\" by Shalev-Shwartz et al., see: https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf.\n",
+    "There's another SVM based algorithm that benefits from the quantum kernel method. Here, we introduce an implementation of a another classification algorithm, which is an alternative version to the `QSVC` available in Qiskit Machine Learning and shown in the [\"Quantum Kernel Machine Learning\"](./03_quantum_kernel.ipynb) tutorial. This classification algorithm implements the Pegasos algorithm from the paper \"Pegasos: Primal Estimated sub-GrAdient SOlver for SVM\" by Shalev-Shwartz et al., see: https://home.ttic.edu/~nati/Publications/PegasosMPB.pdf.\n",
     "\n",
     "This algorithm is an alternative to the dual optimization from the `scikit-learn` package, benefits from the kernel trick, and yields a training complexity that is independent of the size of the training set. Thus, the `PegasosQSVC` is expected to train faster than QSVC for sufficiently large training sets.\n",
     "\n",