Quantum Noise-Induced Reservoir Computing
This work addresses the challenge of noise in quantum devices for researchers and practitioners in quantum computing, offering a novel approach to turn a disadvantage into an advantage, though it appears incremental in applying reservoir computing concepts to quantum noise.
The paper tackles the problem of quantum noise in quantum computing by proposing a framework called quantum noise-induced reservoir computing, showing that quantum noise can be harnessed for useful information processing in temporal data, with results indicating increased information processing capacity at higher noise levels and error rates.
Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We tackle this issue by proposing a framework called quantum noise-induced reservoir computing and show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices' higher noise levels and error rates. Our study opens up a novel path for diverting useful information from quantum computer noises into a more sophisticated information processor.