Training Quantum Recurrent Neural Networks on a Josephson Integrated Circuit

This work is devoted to solving the problem of processing numerical sequences using variational quantum algorithms implemented on a noisy intermediate-scale quantum computer. The quantum computing platform is an integrated circuit based on superconducting artificial atoms. The architecture of the quantum recurrent neural network is constructed using single- and two-qubit operations. At the simulation stage, we investigated the trainability of the model as a function of the number of qubits, as well as the encoded into the qubit states data amount and the encoding method. A comparison with classical architectures showed that on current quantum processors it is possible to achieve a prediction quality on the chosen machine-learning task that is comparable to that of models implemented on classical processors.