Quantum Noise-Induced Reservoir Computing
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.
Tomoyuki Kubota Yudai Suzuki Shumpei Kobayashi Quoc Hoan Tran Naoki Yamamoto Kohei Nakajima