A brief introduction to theory and application for ML
Graphical Representation of a vector, matrix and a rack-3 tensor
Tensor Contraction
Dataset | MPS | MPS + VQC(3) | FRQI (4) + QMPS (2) | QMPS (2) (with PCA -> 4) |
---|---|---|---|---|
Digits (0/1) | 0.888 (10) | 0.9954 (10) | 0.995 (10) | 0.996 (10) |
Fashion (0/1) | 0.908 (10) | 0.955 (10) | - | 0.852 (10) |
Digits (0-5) | 0.217 (10), 0.874 (100) | - | - | - |
Fashion (0-5) | 0.494 (10), 0.800 (100) | - | - | - |
California Housing | 1.5331 (10) | 1.1672 (10) | N/A | - |
Gear Box | 0.8286 (2, 100/10), 0.9000 (20, 100), 1.000 (20, 1000) |
0.77 (2, 10), 0.83 (2, 100), 0.87 (20, 100) |
N/A | 0.89 (10) |
Question 1
What are these unitaries?
Question 2
Best way to get regression values from QC?
Question 3
Best way to get multiple values from QC?