1 research outputs found
Quantum reservoir neural network implementation on a Josephson mixer
Quantum reservoir computing is a promising approach to quantum neural
networks capable of solving hard learning tasks on both classical and quantum
input data. However, current approaches with qubits are limited by low
connectivity. We propose an implementation for quantum reservoir that obtains a
large number of densely connected neurons by using parametrically coupled
quantum oscillators instead of physically coupled qubits. We analyse a specific
hardware implementation based on superconducting circuits. Our results give the
coupling and dissipation requirements in the system and show how they affect
the performance of the quantum reservoir. Beyond quantum reservoir computation,
the use of parametrically coupled bosonic modes holds promise for realizing
large quantum neural network architectures