Quantum reservoir computation utilising scale-free networks

TL;DR

This paper introduces a quantum reservoir computing model using scale-free networks, demonstrating quantum advantage in pattern recognition without the need for programming or optimization, highlighting the potential of quantum complexity for practical applications.

Contribution

The paper presents a novel quantum reservoir computing scheme based on scale-free networks that leverages quantum complexity for pattern recognition, avoiding the need for programming or optimization.

Findings

Demonstrates quantum advantage in pattern recognition tasks.

Utilizes inherent complexity of scale-free networks.

Does not require programming or optimization of the quantum layer.

Abstract

Today's quantum processors composed of fifty or more qubits have allowed us to enter a computational era where the output results are not easily simulatable on the world's biggest supercomputers. What we have not seen yet, however, is whether or not such quantum complexity can be ever useful for any practical applications. A fundamental question behind this lies in the non-trivial relation between the complexity and its computational power. If we find a clue for how and what quantum complexity could boost the computational power, we might be able to directly utilize the quantum complexity to design quantum computation even with the presence of noise and errors. In this work we introduce a new reservoir computational model for pattern recognition showing a quantum advantage utilizing scale-free networks. This new scheme allows us to utilize the complexity inherent in the scale-free networks, meaning we do not require programing nor optimization of the quantum layer even for other computational tasks. The simplicity in our approach illustrates the computational power in quantum complexity as well as provide new applications for such processors.