Feedback Connections in Quantum Reservoir Computing with Mid-Circuit Measurements

Abstract

Existing approaches to quantum reservoir computing can be broadly categorized into restart-based and continuous protocols. Restart-based methods require reinitializing the quantum circuit for each time step, while continuous protocols use mid-circuit measurements to enable uninterrupted information processing. A gap exists between these two paradigms: while restart-based methods naturally have high execution times due to the need for circuit reinitialization, they can employ novel feedback connections to enhance performance. In contrast, continuous methods have significantly faster execution times but typically lack such feedback mechanisms. In this work, we investigate a novel quantum reservoir computing scheme that integrates feedback connections, which can operate within the coherence time of a qubit. We demonstrate our architecture using a minimal example and evaluate memory capacity and predictive capabilities. We show that the correlation coefficient for the short-term memory task on past inputs is nonzero, indicating that feedback connections can effectively operate during continuous processing to allow the model to remember past inputs.

Typos

References in corpus (15)

• Strong and weak thermalization of infinite non-integrable quantum systems
• Harnessing disordered quantum dynamics for machine learning
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• Temporal Information Processing on Noisy Quantum Computers
• Efficient Long-Range Entanglement using Dynamic Circuits
• Time Series Quantum Reservoir Computing with Weak and Projective Measurements
• A Substrate-Independent Framework to Characterise Reservoir Computers
• Natural quantum reservoir computing for temporal information processing
• Learning Nonlinear Input-Output Maps with Dissipative Quantum Systems
• Dissipation as a resource for Quantum Reservoir Computing
• Feedback-driven quantum reservoir computing for time-series analysis
• Microwave signal processing using an analog quantum reservoir computer
• Optimizing quantum noise-induced reservoir computing for nonlinear and chaotic time series prediction
• Extending echo state property for quantum reservoir computing
• Composite FORCE learning of chaotic echo state networks for time-series prediction