Setup Guide for Phase 4 Testing
Quick Start
- Clone the repository:
git clone https://huggingface.co/jmurray10/phase4-quantum-compression
cd phase4-quantum-compression
- Install dependencies:
pip install -r requirements.txt
- Test compressed models:
import torch
# Load compressed model
model = torch.load('models/mlp_compressed_int8.pth')
print(f"Model loaded successfully!")
# Test inference
test_input = torch.randn(1, 784)
output = model(test_input)
print(f"Output shape: {output.shape}")
- Run validation tests:
python tests/test_saved_models.py
python tests/test_compressed_model_usability.py
Available Models
Model | Type | Size | Path |
---|---|---|---|
MLP Original | FP32 | 943KB | models/mlp_original_fp32.pth |
MLP Compressed | INT8 | 241KB | models/mlp_compressed_int8.pth |
CNN Original | FP32 | 1.69MB | models/cnn_original_fp32.pth |
CNN Compressed | INT8 | 483KB | models/cnn_compressed_int8.pth |
Running Quantum Experiments
# Example: Run Grover's algorithm
from src.quantum.qiskit.grover_aer import run_grover_experiment
result = run_grover_experiment(n_qubits=3, marked_state=5)
print(f"Success probability: {result['success_rate']:.3f}")
Energy Measurement
# Example: Measure model energy consumption
from src.energy.energy_logger_nvml import EnergyLogger
logger = EnergyLogger()
energy = logger.measure_inference_energy(model, test_data)
print(f"Energy consumed: {energy:.2f} J")
Reproducing Results
All results can be reproduced by running the scripts in the src/
directory.
No hardcoded values - everything is computed at runtime!