Data Anonymisation with the Density Matrix Classifier

We propose a new data anonymisation method based on the concept of a quantum feature map. The main advantage of the proposed solution is that a high degree of security is combined with the ability to perform classification tasks directly on the anonymised (encrypted) data resulting in the same or even higher accuracy compared to that obtained when working with the original plain text data. This enables important usecases in medicine and finance where anonymised datasets from different organisations can be combined to facilitate improved machine learning outcomes utilising the combined dataset. Examples include combining medical diagnostic imaging results across hospitals, or combining fraud detection datasets across financial institutions. We use the Wisconsin Breast Cancer dataset to obtain results on Rigetti’s quantum simulator and Ankaa-3 quantum processor. We chose this medical dataset for its public availability via the UCI ML repository, though financial datasets are our main focus for anonymisation tests. We compare the results with classical benchmarks and with those obtained from an alternative anonymisation approach using a Restricted Boltzmann Machine to generate synthetic datasets. Finally, we introduce concepts from the theory of quantum magic to optimise the circuit ansatz and hyperparameters used within the quantum feature map.