# Quantum SVM with large feature set

I am trying to practice QSVM from the following tutorial

Introduction into Quantum Support Vector Machines

The author has used 2 feature_dimension with 2 component PCA

feature_dimension =2


Now my question is, why?

Is it because of the limitation of the number of qubits?

When I tried to increase both to 3 the testing success ratio decreased to 0.45

How can I use more feature sets

• PCA is used to reduce the 32 features of the dataset to 2. Therefore, after PCA feature_dimension = PCA_dim = 2. Mar 28 at 23:40
• Yes that I understand. Is there any practical cause of doing that? What is my data has more features? Thanks Mar 29 at 1:43

But if you are limited in the number of qubits, you can change the data encoding. For instance, in this paper Fig.2, they use a quantum circuit with $$17$$ qubits and loaded $$67-$$dimensional data without dimensionality reduction. This results in a deeper circuit.