Permutation Invariant Encodings for Quantum Machine Learning with Point Cloud Data

Understand

Quantum Computing offers a potentially powerful new method for performing Machine Learning.

• However, several Quantum Machine Learning techniques have been shown to exhibit poor generalisation as the number of qubits increases.
• We address this issue by demonstrating a permutation invariant quantum encoding method, which exhibits superior generalisation performance, and apply it to point cloud data (three-dimensional images composed of points).
• Point clouds naturally contain permutation symmetry with respect to the ordering of their points, making them a natural candidate for this technique.