Robust and Scalable Quantum Repeaters Using Machine Learning

Quantum repeaters are integral systems to quantum computing and quantum communication, as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that entangled quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. However, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. Using a swap gate is also possible; however, it is difficult to manually create and implement the quantum algorithm for this circuit as the size of the system increases. Here we propose a different approach: To use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB. Training is then done for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that scale-up is very effective and relatively easy, and the effects of noise and decoherence are reduced as the size of the system increases.