Quantum Kernel Machine

Introduction

Supervised Quantum Machine Intelligence is kernel methods\cite{} or even beyond kernel methods\cite{} is widely described in the associated literature. Our previous work - BayesianQNNs also show some insights into such postulations and the lesson learned from different types of data encoding\cite{}. We show the kernels generated by rotation embeddings\cite{}, with the model depth from $1$ to $4$ in \textbf{Figure}~\ref{fig:kernels}. A random noise with increasing standard deviation from $0.05, 0.1$ to $0.2$ is added to show the decision boundary of the classifiers. Here, we found that adding random noise can make the model decision less conservative as the boundary is slightly vanished by noises. Besides, varying the value of noises significantly shift the decision boundary. Specifically, the standard deviation of parameter-free noise, denoted as $\epsilon$, increases in a left-to-right fashion with values of $0.1$, $0.5$, and $1$. The top row presents the decision boundary and epistemic uncertainty estimation using the first dataset, while the bottom row corresponds to the another dataset. We found that the effect of noise is data-dependent. In the case of the former dataset, there is no significant difference observed. However, for the latter dataset, the decision boundary is noticeably affected when higher values of $\sigma_\epsilon$ are employed.

Neural Architecture of EtaNet

Model Capacity

Bayesian inference of EtaNet

Experiment History

k = 4, l = 4| k = 4, l = 6| k = 4, l = 8| k = 6, l = 4| k = 6, l = 6| k = 6, l = 8| k = 8, l = 4| k = 8, l = 6| k = 8, l = 8|

Some Arts - Colored by Quantum (Red) - Classical (Blue) and Rainbow Plots of -Net

Requirement

python >= 3.6.12, pennylane == 0.13.0, multiprocessing == 2.6.2.1, optuna == 2.4.0, scipy == 1.5.4

Code usage

Training Model End-to-end

cd code
python main.py