Connection between memory performance and optical absorption in quantum reservoir computing

Abstract

The fading memory property is a key requirement for reservoir computers -- a specific type of recurrent neural network with fixed internal weights. While mostly undesired in gate-based quantum computing, dissipation due to material imperfections or coupling to the environment acts as a natural mechanism intrinsically providing fading memory to reservoir computers based on dynamical open quantum systems. In this work, we unravel a connection between the physical metric of optical absorption and the performance of quantum reservoir computers in terms of their short-term memory capacity. We establish this link by considering a coherent input encoding in conjunction with tunable qubit decay, giving precise control over the fading memory in the quantum reservoir computer. Our analysis enables us to identify a sweet-spot regime for the dissipation strength at which memory performance is maximized.

9 pages, 8 figures

References in corpus (15)

• Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator
• A short introduction to the Lindblad Master Equation
• Harnessing disordered quantum dynamics for machine learning
• Quantum reservoir computing with a single nonlinear oscillator
• Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
• Reconstructing quantum states with quantum reservoir networks
• Scalable photonic platform for real-time quantum reservoir computing
• Optimizing Memory in Reservoir Computers
• Dissipation as a resource for Quantum Reservoir Computing
• Exploring quantum mechanical advantage for reservoir computing
• Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks
• Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
• Benefits of Open Quantum Systems for Quantum Machine Learning
• Liouvillian skin effect in quantum neural networks
• Restrictions on Physical Stochastic Reservoir Computers