  Quantum computers can solve specific complex tasks for which no
reasonable-time classical algorithm is known. Quantum computers do however also
offer inherent security of data, as measurements destroy quantum states. Using
shared entangled states, multiple parties can collaborate and securely compute
quantum algorithms. In this paper we propose an approach for distributed
quantum machine learning, which allows multiple parties to securely perform
computations, without having to reveal their data. We will consider a
distributed adder and a distributed distance-based classifier.
