rich7420 opened a new pull request, #1175:
URL: https://github.com/apache/mahout/pull/1175
## Title
[QDP] feat: add SVHN Quantum Kernel SVM benchmark (12 qubits)
## Body
### Related Issues
N/A
### Changes
- [ ] Bug fix
- [x] New feature
- [ ] Refactoring
- [ ] Documentation
- [ ] Test
- [ ] CI/CD pipeline
- [ ] Other
### Why
A Quantum Kernel SVM pipeline eliminates the iterative training loop —
encoding becomes ~19% of total pipeline time, making QDP's GPU encoding
advantage clearly visible in end-to-end results.
### How
Added two new benchmark scripts that implement a Quantum Kernel SVM
classification pipeline on SVHN:
```
SVHN (32×32×3) → StandardScaler → Amplitude Encode (3072→4096, 12 qubits)
→ Quantum Kernel K[i,j] = |⟨ψ(xⱼ)|ψ(xᵢ)⟩|² → SVM (precomputed) → 5-fold CV
```
#### New files
- `pennylane_baseline/svhn_kernel_amplitude.py` — CPU encoding (L2-norm +
zero-pad)
- `qdp_pipeline/svhn_kernel_amplitude.py` — QDP GPU encoding (amplitude)
#### Benchmark results (RTX 3080)
5000 samples, 5-fold stratified CV, binary classification (digit 1 vs 7),
C=100
| Step | pennylane | QDP GPU | Speedup |
|------|-----|---------|---------|
| Scale + Encode | 0.648s | 0.342s | **1.89×** |
| Kernel (matmul) | 1.191s | 0.788s | **1.51×** |
| CV fit + predict (5 folds) | 1.597s | 1.170s | 1.36× |
| **Total** | **3.44s** | **2.30s** | **1.49×** |
**Accuracy: 0.9104 ± 0.0091** (identical for both pipelines)
#### How to run
```bash
# CPU baseline
python
benchmark/encoding_benchmarks/pennylane_baseline/svhn_kernel_amplitude.py
# QDP GPU pipeline
python benchmark/encoding_benchmarks/qdp_pipeline/svhn_kernel_amplitude.py
# Custom parameters
python benchmark/encoding_benchmarks/qdp_pipeline/svhn_kernel_amplitude.py \
--n-samples 10000 --folds 5 --svm-c 100
```
> SVHN data (~180 MB) is auto-downloaded on first run.
## Checklist
- [x] Added or updated unit tests for all changes
- [x] Added or updated documentation for all changes
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