Efficient privacy-preserving training of quantum neural networks through ensemble encoding of quantum states

Quantum computing is gaining popularity due to its potential to detect complex patterns in data by leveraging unique quantum phenomena. It is particularly promising for complex data applications, such as those in biomedicine. However, in these contexts, it is often necessary to share and then aggregate data from multiple participants to achieve sufficient statistical power. Sharing data, especially sensitive information such as personal medical records, raises significant privacy concerns. To address these challenges, we propose a quantum-native method for encoding entire data cohorts directly into special quantum states, which we refer to as composite states. These quantum states provide a generic encoding suitable for a wide variety of downstream computations while preventing the inference of individual-level information. Quantum computations can be performed directly on composite states without accessing the underlying raw data. Building on this, we present the theoretical foundations of our scheme's utility and privacy guarantees, demonstrating resistance to membership inference attacks as measured via differential privacy. Furthermore, we introduce protocols that support multiparty collaborative quantum neural network training across diverse domains. Finally, we validate the effectiveness of composite states on three different datasets, focusing on genomic studies while also indicating how the approach can be applied in other domains without adaptation.