Abstract We introduce a formal modeling methodology to analyze quantum communication protocols in the tool Uppaal . Our approach encodes quantum states, operations, and measurements into Uppaal timed automata with data extensions and external function calls, enabling both exhaustive verification in the ideal (noiseless) case and statistical model checking for realistic noisy scenarios. We apply our framework to the Beyond Superdense Coding protocol—a time-slotted variant of superdense coding—combined with quantum entanglement distillation, and demonstrate that Uppaal can deal with these protocols even under complex timing and decoherence constraints.

Consider two data providers that want to contribute data to a certain learning model. Recent works have shown that the value of the data of one of the providers is dependent on the similarity with the data owned by the other provider. It would thus be beneficial if the two providers can calculate the similarity of their data, while keeping the actual data private. In this work, we devise multiparty computation-protocols to compute similarity of two data sets based on correlation, while offering controllable privacy guarantees. We consider a simple model with two participating providers and develop methods to compute exact and approximate correlation, respectively, with controlled information leakage. Both protocols have computational and communication complexities that are linear in the number of data samples. We also provide general bounds on the maximal error in the approximation case, and analyse the resulting errors for practical parameter choices.