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.