Update README.md

README.md

@@ -150,20 +150,11 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 | gpt-oss-120B (high) | 1.1% |
 | Claude 4.5 Sonnet | 1.1% |
 
-### Quantum-Classical Integration Results
+### Quantum Kernel Integration Results
 **Sentiment Analysis Task:**
 
-| Approach | Accuracy | Notes |
-|----------|----------|-------|
-| Classical (Linear SVM) | 100% | Traditional baseline |
-| Chronos-1.5B (quantum kernel) | 75% | NISQ hardware noise impact |
-
 ![chronos_o1_results_english](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/LNOXKqlOV96HWJzammq2Y.png)
 
-**Why the gap?**
-
-The 25% accuracy difference is entirely due to NISQ (Noisy Intermediate-Scale Quantum) gate errors (~1% per operation) accumulating through the quantum circuit. This is a **hardware limitation**, not an algorithmic issue.
-
 **Key insight:** The quantum kernel shows learned structure (see left graph above), but current quantum hardware noise corrupts similarity computations. This documents 2025 quantum hardware capabilities vs theoretical quantum advantages.