Quantum-Inspired Reservoir Computing Using Simulated Spintronic Networks

Overview

This project develops a physics-grounded framework for reservoir computing implemented in simulated spintronic networks (STNO arrays, vortex MTJs and skyrmion textures). By deriving semi-analytic relationships between Landau–Lifshitz–Gilbert (LLG) parameters (damping, anisotropy, coupling) and reservoir primitives (nonlinearity, fading memory, separability), constructing a reproducible benchmark suite (SpinRC-Bench), and evaluating robustness, scalability, and an energy–latency metric derived from device physics, the study will show whether and when simulated spintronic reservoirs can outperform classical echo state networks (ESNs) and lightweight neural networks on temporal tasks, and will prescribe device-level design rules for future hardware.

Competition history

• CSEF 2026 Physics & Astronomy (Senior Division) · Entry S-17-02

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