講演情報
[15p-K406-2]Modular Quantum Extreme Reservoir Computing
〇(PC)Hon Wai Lau1, Aoi Hayashi2,1,3, Akitada Sakurai1, William John Munro1, Kae Nemoto1,3 (1.OIST, 2.SOKENDAI, 3.NII)
キーワード：
Quantum machine learning、Reservoir computing、Quantum computing
The connectivity between qubits plays a crucial role in the performance of quantum extreme reservoir computing (QERC), particularly regarding long-range and inter-modular connections. We demonstrate that sufficiently long-range connections within a single module can achieve performance comparable to fully connected networks in supervised learning tasks. Further analysis of inter-modular connection schemes -- such as boundary, parallel, and arbitrary links -- shows that even a small number of well-placed connections can significantly enhance QERC performance. These findings suggest that modular QERC architectures, which could be more easily implemented on two-dimensional quantum chips or through the integration of small quantum systems, provide an effective approach for machine learning tasks.