Solid-state NMR implementation of quantum reservoir computing

Reservoir computing is a framework for computation using a neural network (the reservoir), where the internode transition is not trained but instead linear readout weight is trained. Recently a quantum counterpart of reservoir computing was proposed. Here, we implement quantum reservoir computing with nuclear spin qubits as network nodes. Our ensemble qubit system is comprised of 1H and 13C spins in l-alanine-1,13C diluted into a single crystal of l-alanine-2H7. The qubit state is transited with the dipole-dipole interactions. Data input is represented by the phase of NMR pulse sequence. 13C spin state is read out by NMR signal. In this talk, we show the results and discuss the scalability of the architecture.