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I'm implementing a quantum support vector machine on qiskit and I was wondering what the quantum part of the algorithm actually does. I'm aware that it's a feature map that executes the kernel function but what is the significance of using quantum computers for that if the kernel trick is already effective classically? And how does the quantum feature map work?

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The basic idea of how the quantum feature map works is that you're using a quantum computer to map each input datapoint $x$ from your training domain $\mathcal{X}$ into a quantum state $|\phi(x)\rangle = U(x)|0\rangle$ in the (presumably) high dimensional quantum state space, and then evaluating a set of kernel functions:

$$ k_Q(x_i, x_j) = |\langle 0|U(x_j)^\dagger U(x_i)|0\rangle|^2 $$

for all pairs $x_i,x_j \in \mathcal{X}$. The Support Vector Machine (SVM) that classifies $\mathcal{X}$ can then be treated as a black box that takes in the kernel matrix $K_{ij} = k_Q(x_i, x_j)$ and returns a model $f_Q: \mathcal{X} \rightarrow \{0,1\}$.

The "kernel trick" is the substitution that allows us to use the SVM (ordinarily a linear classifier on $\mathcal{X}$) to classify the data using $K$ to achieve non-linear decision boundaries. But regardless of whether $K$ is generated by a classical or a quantum computer, this doesn't guarantee an effective classifier. An example of a kernel that can be shown to fail is the quadratic polynomial kernel

$$ k_C(x_i, x_j) = (\langle x_i, x_j \rangle + b)^2 $$

which will be generally incapable of classifying data that is labelled by a function of degree 3 or higher. So if you can find a quantum kernel $k_Q$ that results in a kernelized SVM that successfully classifies data labeled by those functions for which $k_C$ fails, you've found at least some evidence to support the use of your quantum feature map.

More generally, the motivation to use quantum feature maps is that they might be more expressive than some classical counterparts. For instance (Schuld, 2020) uses Fourier analysis to connect the spectrum of a classifier $f_Q$ to the number of local rotations in the circuit $U(x)$.

But justifying the use of quantum kernels also requires finding a feature map that is inefficient to compute classically, otherwise you would just simulate the unitaries $U(x) \forall x$ to evaluate your kernels$^\dagger$. Some recent work (Huang, 2020) takes steps towards evaluating the power of quantum kernels compared to some classical counterparts but overall this is still a very open question.


$^\dagger$ keep in mind that if you can simulate $U(x)$ efficiently then you can do "one-shot" evaluation of the kernel matrix so that the number of circuit simulations is only $O(n)$ instead of the $O(n^2)$ needed to evaluate $k_Q(x, x')$ on hardware. This raises the bar for demonstrating speedup using this kind of quantum SVM.

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  • $\begingroup$ How is the kernel matrix an input? $\endgroup$ Jan 24 at 4:46
  • $\begingroup$ What is a kernelized SVM? $\endgroup$ Jan 24 at 11:25
  • $\begingroup$ Hey Forky so I'm trying to get a high level understanding of what the QC does so correct me if I'm wrong here. In a SVM, the classical computer computes through kernel functions of n degrees to find the most suitable space with a clear hyperplane dividing the two classes. But since some datasets can be computationally expensive to calculate the kernels of (or inner products with the kernel trick), quantum computers can efficiently apply that kernel function to obtain a speedup(theoretically). $\endgroup$ Jan 24 at 11:32
  • $\begingroup$ "The 'kernel trick' is the substitution that allows us to use the SVM (ordinarily a linear classifier on $X$) to classify the data using $K$ to achieve non-linear decision boundaries." --> I thought that the kernel trick was used to efficiently determine the utility of casting data points to a higher dimension through calculating the inner products of each pair. $\endgroup$ Jan 24 at 11:36
  • $\begingroup$ The most basic formulation of an SVM is as a constrained optimization problem that looks for a linear ("hyperplane") boundary that separates two classes of linearly separable data while enforcing that the chosen hyperplane maximizes its perpendicular distance ("margin") from members of either class. After some rearranging this can be stated as a maximization problem w.r to a Lagrangian $L = \sum_i \alpha_i - \sum_{i,j} \alpha_i \alpha_j y_i y_j \langle x_i, x_j \rangle$. The "kernel trick" refers to substituting the $\langle x_i , x_j \rangle$ for a positive definite symmetric $k(x_i, x_j)$ $\endgroup$
    – forky40
    Jan 24 at 20:57
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Short answer: SVMs and Quantum SVMs are the same except you use kernels that are not efficient to compute using a classical computer but that can be computed efficiently using a quantum computer. There haven't been any smoking-gun use cases so far but a very interesting paper on proof-of-concept example can be found here: https://arxiv.org/abs/2010.02174

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