Optimization of the Memory Reset Rate of a Quantum Echo-State Network
  for Time Sequential Tasks
Quantum reservoir computing is a class of quantum machine learning algorithms
involving a reservoir of an echo state network based on a register of qubits,
but the dependence of its memory capacity on the hyperparameters is still
rather unclear. In order to maximize its accuracy in time--series predictive
tasks, we investigate the relation between the memory of the network and the
reset rate of the evolution of the quantum reservoir. We benchmark the network
performance by three non--linear maps with fading memory on IBM quantum
hardware. The memory capacity of the quantum reservoir is maximized for central
values of the memory reset rate in the interval [0,1]. As expected, the memory
capacity increases approximately linearly with the number of qubits. After
optimization of the memory reset rate, the mean squared errors of the predicted
outputs in the tasks may decrease by a factor ~1/5 with respect to previous
implementations.
Riccardo Molteni Claudio Destri Enrico Prati