Basic performance comparison between classical and quantum machine learning

Comparison between classical and quantum machine learning using the .exe published versions below.

Environment configurations:

OS=Windows 10.0.19044.2006 (21H2)
Intel Core i5-8250U CPU 1.60GHz (Kaby Lake R), 1 CPU, 8 logical and 4 physical cores
.NET SDK=6.0.401

🎲 Dataset

HouseData { Size = 1.1F, Price = 1.2F, IsExpensive = false }
HouseData { Size = 1.3F, Price = 1.5F, IsExpensive = false }
HouseData { Size = 1.5F, Price = 1.7F, IsExpensive = false }
HouseData { Size = 1.7F, Price = 1.8F, IsExpensive = false }
HouseData { Size = 1.9F, Price = 2.0F, IsExpensive = false }
HouseData { Size = 2.1F, Price = 2.2F, IsExpensive = false }
HouseData { Size = 2.3F, Price = 2.4F, IsExpensive = true }
HouseData { Size = 2.5F, Price = 2.7F, IsExpensive = true }
HouseData { Size = 2.7F, Price = 2.8F, IsExpensive = true }
HouseData { Size = 2.9F, Price = 2.9F, IsExpensive = true }
HouseData { Size = 3.1F, Price = 3.2F, IsExpensive = true }

Validation data:

HouseData { Size = 2.3F, Price = 2.5F }

⚗️ Results Summary

Classic ML

Predicted price for size: 213.75m2 and price: $1,250,000.00. Is expensive? True
Elapsed=00:00:05.1474523

Quantum ML

Training complete, found optimal parameters: [0.74855,0.86259,0.50246,1], -0.33899029094962474 with 7 misses
Model: SequentialModel(([ControlledRotation(((0, []), PauliY, 0))], [0.74855,0.86259,0.50246,1], -0.33899029094962474))
Result: 1.00000.
Elapsed=00:00:00.9521979

The Quantum algorithm was about 5x faster than the classic version.