Toward NMR Quantum Reservoir Computing

Makoto Negoro, Keisuke Fujii, Kohei Nakajima +2 more

Claudio Conti

Jonathan A. Jones

Makoto Negoro, Kosuke Mitarai, Keisuke Fujii +2 more

We experimentally demonstrate quantum machine learning using NMR based on a framework of quantum reservoir computing. Reservoir computing is for exploiting natural nonlinear dynamics with large degrees of freedom, which is called a reservoir, for a machine learning purpose. Here we propose a concrete physical implementation of a quantum reservoir using controllable dynamics of a nuclear spin ensemble in a molecular solid. In this implementation, we demonstrate learning of nonlinear functions with binary or continuous variable inputs with low mean squared errors. Our implementation and demonstration paves a road toward exploiting quantum computational supremacy in NMR ensemble systems for information processing with reachable technologies.

Syed Muntasir Mamun

The prevailing paradigm in artificial intelligence has long privileged scale—parameter count, network depth, and architectural complexity—as the primary driver of performance gains. This paper presents a deep anatomical survey of a recent experimental breakthrough that fundamentally challenges this assumption. Hou et al. (2026) demonstrate that a quantum reservoir computer comprising only nine interacting nuclear spins, realized in a ¹³C-labeled crotonic acid molecule via nuclear magnetic resonance (NMR), achieves superior accuracy in time-series prediction tasks compared to classical echo-state networks (ESNs) with thousands of nodes. Leveraging the natural many-body quantum dynamics of correlated spins—including coherent interactions, entanglement, and dissipative processes—the system exploits the geometry of its high-dimensional Hilbert space and intrinsic constraints on state evolution rather than engineered control or post-hoc optimization. On the NARMA benchmark, it reduces prediction errors by one to two orders of magnitude relative to prior quantum reservoir computing (QRC) experiments. In real-world multi-day weather forecasting (temperature and humidity trends), it outperforms classical reservoirs scaled to thousands Mamun, SM (2026)………………………………………..1 High-Accuracy Temporal Prediction via Emergent Dynamics in Compact Quantum Reservoirs: A Nine-Atom Spin System Outperforming Large-Scale Classical Models (Vol-I) K S C of nodes. Also available on Academia.edu: https://www.academia.edu/attachments/132434219/download_file?s=portfolio