Exploring quantum mechanical advantage for reservoir computing

Abstract

Quantum reservoir computing is an emerging field in machine learning with quantum systems. While classical reservoir computing has proven to be a capable concept of enabling machine learning on real, complex dynamical systems with many degrees of freedom, the advantage of its quantum analogue is yet to be fully explored. Here, we establish a link between quantum properties of a quantum reservoir, namely entanglement and its occupied phase space dimension, and its linear short-term memory performance. We find that a high degree of entanglement in the reservoir is a prerequisite for a more complex reservoir dynamics that is key to unlocking the exponential phase space and higher short-term memory capacity. We quantify these relations and discuss the effect of dephasing in the performance of physical quantum reservoirs.

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Cited by in corpus (10)

• Harnessing Quantum Extreme Learning Machines for image classification
• Role of coherence in many-body Quantum Reservoir Computing
• Stochastic Reservoir Computers
• Quantum reservoir computing on random regular graphs
• Generating Quantum Reservoir State Representations with Random Matrices
• Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
• From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing
• Connection between memory performance and optical absorption in quantum reservoir computing
• The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators
• Quantum reservoir computing in Jaynes-Cummings models: Nonlinear memory and time-series prediction