Update README.md



README.md

@@ -10,7 +10,7 @@ license: apache-2.0
 ---
 
 # Nighthawk-QCNN 🦅
-
+![night](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/Knx2N1lGc6flPfK2mSeQy.jpeg)
 96-qubit Quantum Convolutional Neural Network (QCNN) trained end-to-end on real IBM Quantum Heron r2/r3 hardware (backend: ibm_fez/ibm_kingston) on February 4, 2026.
 
 ## Task
@@ -29,9 +29,21 @@ Binary classification of parity of random Pauli-X excitations in 1D cluster stat
 - **Shots per evaluation**: 384
 - **Optimizer**: SPSA, 12 iterations
 - **Final loss (MSE)**: 0.2704 (after 36 evaluations)
-- **QPU time**: ~7 minutes (IBM Heron r2, Open Plan, excluding queue)
+- **QPU time**: ~7 minutes (IBM Heron r2/r3)
 - **Backend**: ibm_fez (156 qubits, heavy-hex lattice, tunable couplers)
 
+## Training Convergence
+
+![Loss Curve](loss_curve.png)
+MSE loss starts at ~0.268, dips to ~0.243 around evaluation 1.0, then rises again due to noise accumulation.
+
+| Run | Qubits | Samples | Shots | Iterations | Final Loss | QPU Time |
+|-----|--------|---------|-------|------------|------------|----------|
+| 1   | 96     | 16      | 256   | 8          | 0.29       | ~2 min   |
+| 2   | 96     | 24      | 384   | 12         | 0.2704     | ~7 min   |
+
+
+
 ## Repository Files
 
 - `Nighthawk.npy` — trained parameters (72 values)
@@ -52,7 +64,8 @@ qcnn = qasm3.loads(open("qcnn.qasm").read())
 qcnn.assign_parameters(theta)
 
 print("Model loaded. Number of parameters:", len(theta))
-# Next: compose with preparation circuit + run via Sampler
+# Next: compose with preparation circuit + run via Sampler 
+```
 
 ## Notes
 
@@ -64,5 +77,3 @@ print("Model loaded. Number of parameters:", len(theta))
   - Improved connectivity → fewer SWAPs during transpilation → lower overall error accumulation
   - Expected: noticeably lower final loss and higher effective classification accuracy
 - Improvements: more shots, error mitigation (twirling/M3), run on Nighthawk (square lattice).
-
-Ready for Hugging Face upload.