Update README.md

README.md

@@ -31,7 +31,7 @@ base_model:
 
 - **VibeThinker-1.5B** as the base transformer model for embedding extraction
 - **Quantum Kernel Methods** for similarity computation
-- **125-qubit quantum circuits** for enhanced feature space representation
+- **2-qubit quantum circuits** for enhanced feature space representation
 
 This model demonstrates a proof-of-concept for hybrid quantum-classical machine learning applied to sentiment analysis.
 
@@ -44,7 +44,7 @@ Chronos 1.5B supports multiple quantum kernel execution modes:
 | Feature                            | Implementation                                                                 |
 |------------------------------------|---------------------------------------------------------------------------------|
 | Real quantum training              | Quantum rotation angles were optimized on IBM **Heron r2** (`ibm_fez`) in 2025 |
-| Saved quantum parameters           | `quantum_kernel.pkl` — trained 125-qubit gate angles (pickle)                    |
+| Saved quantum parameters           | `quantum_kernel.pkl` — trained 2-qubit gate angles (pickle)                    |
 | Quantum circuit definition         | Available in `k_train_quantum.npy` / `k_test_quantum.npy` (future use)          |
 | Current inference                  | Classical simulation using the trained quantum angles (via cosine similarity)  |
 | True quantum execution (optional)  | Possible by loading `quantum_kernel.pkl` + circuit files and running on IBM Quantum (example scripts will be added) |
@@ -61,7 +61,7 @@ Chronos 1.5B supports multiple quantum kernel execution modes:
 - **Parameters**: ~1.5B
 - **Context Length**: 131,072 tokens
 - **Embedding Dimension**: 1536
-- **Quantum Component**: 125-qubit kernel
+- **Quantum Component**: 2-qubit kernel
 - **Training Data**: 8 sentiment examples (demonstration)
 
 ## Performance