Spin-Network Quantum Reservoir Computing with Distributed Inputs: The Role of Entanglement

TL;DR

This study explores how entanglement in a spin-network quantum reservoir influences memory capacity, revealing that moderate entanglement between input qubits enhances short-term memory performance in distributed input scenarios.

Contribution

It demonstrates the importance of bipartite entanglement, especially between input qubits, in optimizing quantum reservoir computing performance with distributed inputs.

Findings

Maximum memory capacity occurs at small coupling strengths.

Entanglement between input qubits is strongest and most relevant for performance.

Reservoir exhibits extended memory tail at optimal coupling, with performance depending on finite propagation time.

Abstract

Reservoir computing is a promising neuromorphic paradigm, and its quantum implementation using spin networks has shown some advantage when entanglement is present. Here, we consider a distributed scenario in which two distinct input time series are injected into separate qubits of a spin-network reservoir. We investigate how the overall entanglement, as well as its localization in the system, influence the performance of the reservoir. Focusing on bilinear memory tasks that require computing the product of the two inputs, we evaluate the short-term memory capacity and correlate it with logarithmic negativity as a measure of bipartite entanglement. We find that short-term memory capacity reaches its maximum at relatively small coupling strengths. In contrast, average entanglement peaks at larger couplings. Analyzing entanglement across all bipartitions, we find that the entanglement between the two input qubits is consistently the strongest and most relevant for task performance. In the small coupling strength regime where the short-term memory capacity is maximized, the reservoir exhibits an extended memory tail: performance remains high for a long time. Finally, a pronounced dip in performance at zero time delay, observed across frequencies, indicates that information requires a finite propagation time through the reservoir before it can be effectively recalled. In summary, our results show that moderate entanglement, particularly between the two input qubits, plays a key role in enhancing short-term memory performance.