  We propose a method for learning temporal data using a parametrized quantum
circuit. We use the circuit that has a similar structure as the recurrent
neural network which is one of the standard approaches employed for this type
of machine learning task. Some of the qubits in the circuit are utilized for
memorizing past data, while others are measured and initialized at each time
step for obtaining predictions and encoding a new input datum. The proposed
approach utilizes the tensor product structure to get nonlinearity with respect
to the inputs. Fully controllable, ensemble quantum systems such as an NMR
quantum computer is a suitable choice of an experimental platform for this
proposal. We demonstrate its capability with Simple numerical simulations, in
which we test the proposed method for the task of predicting cosine and
triangular waves and quantum spin dynamics. Finally, we analyze the dependency
of its performance on the interaction strength among the qubits in numerical
simulation and find that there is an appropriate range of the strength. This
work provides a way to exploit complex quantum dynamics for learning temporal
data.
