delete LQ-PTQ

Author: KazunoriSumiya

tutorials/notebooks/mct_features_notebooks/keras/example_keras_mct_wrapper.ipynb

@@ -10,7 +10,7 @@
     "[Run this tutorial in Google Colab](https://colab.research.google.com/github/SonySemiconductorSolutions/mct-model-optimization/blob/main/tutorials/notebooks/mct_features_notebooks/keras/example_keras_mct_wrapper.ipynb)\n",
     "\n",
     "## Overview \n",
-    "This notebook provides a comprehensive demonstration of the MCT (Model Compression Toolkit) Wrapper API functionality, showcasing five different quantization methods on a MobileNetV2 model. The tutorial systematically compares the implementation, performance characteristics, and accuracy trade-offs of each quantization approach: PTQ (Post-Training Quantization), PTQ with Mixed Precision, GPTQ (Gradient-based PTQ), GPTQ with Mixed Precision, and LQ-PTQ (Low-bit Quantizer PTQ). Each method utilizes the unified MCTWrapper interface for consistent implementation and comparison.\n",
+    "This notebook provides a comprehensive demonstration of the MCT (Model Compression Toolkit) Wrapper API functionality, showcasing five different quantization methods on a MobileNetV2 model. The tutorial systematically compares the implementation, performance characteristics, and accuracy trade-offs of each quantization approach: PTQ (Post-Training Quantization), PTQ with Mixed Precision, GPTQ (Gradient-based PTQ), GPTQ with Mixed Precision. Each method utilizes the unified MCTWrapper interface for consistent implementation and comparison.\n",
     "\n",
     "## Summary\n",
     "1. **Environment Setup**: Import required libraries and configure MCT with MobileNetV2 model\n",