Quantum Kernel Based Data Fusion

For quantum classification methods, the quantum kernel has a role in encoding the data set from its original low-dimensional real space into a high-dimensional quantum state space utilizing quantum circuits. Finding quantum kernels that provide an advantage in real-world data classification is a major challenge, especially when dealing with heterogeneous data or a large data set, requiring more qubits than are available on current devices. Here, we propose two new methods: first, we implement a multiple kernel method for data coming from multiple sources, and second, we propose to combine quantum chips to process larger data sets. The latter can be realized by splitting larger quantum circuits into smaller sub-circuits. We experimentally implement a multiple quantum kernel approach for different data sets on IBM quantum computers and benchmark their results.