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
Practically, it can be (quite often) a limitation of number of qubits/hardware, but also it is a hyperparameter to play with. So it may be that using more qubits gives you better results or worse.
Also, in the QSVM, there is or may be a parameterized part you have to optimize over. So increasing the number of qubits results in more optimization (more parameters), that makes it harder. You may need to play on the depth of the variational part to improve results (so more parameters to optimize).
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.
Maybe this article can help you: 'Automatic design of quantum feature maps', Sergio Altares-López, Angela Ribeiro and Juan José García-Ripoll, 19 August 2021 - https://doi.org/10.1088/2058-9565/ac1ab1.
They describe a technique to generate optimal quantum feature maps by using multiobjetives genetic algorithms. While the first objective is to increase the accuracy in the predictions on unseen data, building robust classifiers with generalisation power; the second objective of this technique is to reduce the complexity of the quantum circuits.
In this study they utilize 22 features in a quantum model, so it might help you.
