Programmable memristive circuits for reconfigurable collective pseudomagnetic ordering, transport, and learning
Abstract
Programmable hardware typically uses fixed architectures, whereas complex physical systems obtain diverse behavior by connecting reusable local elements through different interaction rules. Memristive devices combine writable states with nonlinear dynamics, but translating these local properties into reconfigurable collective order and symmetry-governed transport remains challenging. Here we develop modular circuits configured through programmable connections between identical memristive units. Experiments establish binary conductance states as Ising-type pseudospins and show that signed feedback between units selects uniform or staggered pseudospin order. Circuit-model simulations then examine controllable domain-wall motion and pattern recovery. A symmetry encoder combines measured conductance patterns with sublattice labels and a selected symmetry rule to configure a fixed transport layer, implementing circuit analogs of ferromagnetic, antiferromagnetic, and altermagnetic transport symmetries. Although the antiferromagnetic and altermagnetic encodings share a compensated pattern, their distinct voltage spreading illustrates how programmed circuit symmetry affects transport. Device-calibrated simulations show that memristive dynamics generates history-dependent features for reservoir computing. By separating local memory from programmable interactions, symmetry encoding and task-specific readout, this architecture provides a modular route from device dynamics to collective order, symmetry-dependent transport, and learning.
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BibTeX
@misc{qiu2026programmable,
title = {{Programmable memristive circuits for reconfigurable collective pseudomagnetic ordering, transport, and learning}},
author = {Sichang Qiu and Yuxin Jiang and Yongchao Wu and Peng Han and Xinxin Gao and Zekun Huang and Ce Shang and Ronny Thomale and Tie Jun Cui and Shuo Liu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.15346},
eprint = {2610.15346},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.15346},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Programmable memristive circuits for reconfigurable collective pseudomagnetic ordering, transport, and learning
AU - Sichang Qiu
AU - Yuxin Jiang
AU - Yongchao Wu
AU - Peng Han
AU - Xinxin Gao
AU - Zekun Huang
AU - Ce Shang
AU - Ronny Thomale
AU - Tie Jun Cui
AU - Shuo Liu
PY - 2026
DA - 2026/10/11
PB - arXiv
DO - 10.48550/arxiv.2610.15346
UR - https://arxiv.org/abs/2610.15346
AB - Programmable hardware typically uses fixed architectures, whereas complex physical systems obtain diverse behavior by connecting reusable local elements through different interaction rules. Memristive devices combine writable states with nonlinear dynamics, but translating these local properties into reconfigurable collective order and symmetry-governed transport remains challenging. Here we develop modular circuits configured through programmable connections between identical memristive units. Experiments establish binary conductance states as Ising-type pseudospins and show that signed feedback between units selects uniform or staggered pseudospin order. Circuit-model simulations then examine controllable domain-wall motion and pattern recovery. A symmetry encoder combines measured conductance patterns with sublattice labels and a selected symmetry rule to configure a fixed transport layer, implementing circuit analogs of ferromagnetic, antiferromagnetic, and altermagnetic transport symmetries. Although the antiferromagnetic and altermagnetic encodings share a compensated pattern, their distinct voltage spreading illustrates how programmed circuit symmetry affects transport. Device-calibrated simulations show that memristive dynamics generates history-dependent features for reservoir computing. By separating local memory from programmable interactions, symmetry encoding and task-specific readout, this architecture provides a modular route from device dynamics to collective order, symmetry-dependent transport, and learning.
ER -