Reservoir Computing Using Measurement-Controlled Quantum Dynamics
This work addresses the need for efficient and error-tolerant forecasting in resource-limited conditions, representing an incremental advance in quantum reservoir computing.
The paper tackles the problem of forecasting highly nonlinear and chaotic phenomena by introducing a quantum reservoir computing system using a probed atom in a cavity with measurement-controlled quantum dynamics, achieving fast and reliable forecasts with a small number of artificial neurons compared to traditional methods.
Physical reservoir computing (RC) is a machine learning algorithm that employs the dynamics of a physical system to forecast highly nonlinear and chaotic phenomena. In this paper, we introduce a quantum RC system that employs the dynamics of a probed atom in a cavity. The atom experiences coherent driving at a particular rate, leading to a measurement-controlled quantum evolution. The proposed quantum reservoir can make fast and reliable forecasts using a small number of artificial neurons compared with the traditional RC algorithm. We theoretically validate the operation of the reservoir, demonstrating its potential to be used in error-tolerant applications, where approximate computing approaches may be used to make feasible forecasts in conditions of limited computational and energy resources.