Dynamical Phase Transitions in Quantum Reservoir Computing

Abstract

Closed quantum systems exhibit different dynamical regimes, like Many-Body Localization or thermalization, which determine the mechanisms of spread and processing of information. Here we address the impact of these dynamical phases in quantum reservoir computing, an unconventional computing paradigm recently extended into the quantum regime that exploits dynamical systems to solve nonlinear and temporal tasks. We establish that the thermal phase is naturally adapted to the requirements of quantum reservoir computing and report an increased performance at the thermalization transition. Uncovering the underlying physical mechanisms behind optimal information processing capabilities of spin networks is essential for future experimental implementations and provides a new perspective on dynamical phases.

Nonlinear input transformations are ubiquitous in quantum reservoir computing

“…where P in is the number of sub-components used for input. This is the input encoding employed in a majority of QRC studies to date [11][12][13][14][15][16][17][18][19], and although not a universal convention, we consider the re-initialized input nodes to be part of the reservoir system. Even when they are considered separate, for the purposes of our study they can still be thought of as input pre-processing.…”

Quantum Reservoir Computing for Speckle Disorder Potentials

“…From the point of view of the quantum reservoir, a given speckle potential corresponds to an external time-dependent signal fed at discrete times, t = k∆t. The input at a given time, V k , is encoded in one of the qubits of the system (see Figure 1), qubit 1, by setting its state as [24,39,54,55]:…”

“…We work on a system of units with h = 1 and J s = 1. The time intervals ∆t, are expressed in units of 1/J s , and h, that correspond to an external magnetic field in the z direction, fixed at h = 10J s , such that the system is in the appropriate dynamical regime [54]. This kind of system was in the original proposal of QRC in [24] and has been extensively studied for information processing purposes in further several works [39,[54][55][56][57][58][59].…”

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

“…Furthermore quantum substrates can interact with quantum inputs and are naturally suited for quantum tasks, providing new avenues for edge computing in quantum systems with increasing complexity. Beyond the opportunities in the context of quantum technologies for computation with a quantum advantage, QRC and QELM offer a new and original perspective to characterize quantum physical systems in terms of their operation as substrates [46].…”