Geometric Approach to Zero-Memory Quantum Dot Reservoir Computing

arXiv:2606.29320v2 Announce Type: replace-cross Abstract: Physical reservoir computing offers an energy-efficient alternative to conventional neural networks, where the material-specific intrinsic memory capacity in a physical system plays an indispensable role. Substituting temporal memory with spatial degrees of freedom, we demonstrate that the memory capacity can be created extrinsically in systems with no intrinsic memory by exploiting the computational space-time tradeoff. Our approach utilizes multidimensional input nodes to function as a spatial memory axis, thereby replacing the dependency on intrinsic history-dependent dynamics in the reservoir. Our scheme is validated in a multi-terminal quantum dot system, whose discrete energy levels provide strong nonlinearity and complexity crucial for reservoir computing, while its short relaxation time leaves no room for intrinsic memory. Numerically, the quantum dot reservoir with a tunable extrinsic memory shows high performance on both chaotic future prediction and nonlinear transformation tasks. Furthermore, from the analysis of quantum state trajectory acquired from task operations, the geometric understanding of the extrinsic memory capacity, nonlinearity, and complexity is provided and their correlations are systematically investigated.

Abstract:Physical reservoir computing offers an energy-efficient alternative to conventional neural networks, where the material-specific intrinsic memory capacity in a physical system plays an indispensable role. Substituting temporal memory with spatial degrees of freedom, we demonstrate that the memory capacity can be created extrinsically in systems with no intrinsic memory by exploiting the computational space-time tradeoff. Our approach utilizes multidimensional input nodes to function as a spatial memory axis, thereby replacing the dependency on intrinsic history-dependent dynamics in the reservoir. Our scheme is validated in a multi-terminal quantum dot system, whose discrete energy levels provide strong nonlinearity and complexity crucial for reservoir computing, while its short relaxation time leaves no room for intrinsic memory. Numerically, the quantum dot reservoir with a tunable extrinsic memory shows high performance on both chaotic future prediction and nonlinear transformation tasks. Furthermore, from the analysis of quantum state trajectory acquired from task operations, the geometric understanding of the extrinsic memory capacity, nonlinearity, and complexity is provided and their correlations are systematically investigated.

Disordered Systems and Neural Networks (cond-mat.dis-nn); Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Quantum Physics (quant-ph)

arXiv:2606.29320 [cond-mat.dis-nn]

(or arXiv:2606.29320v2 [cond-mat.dis-nn] for this version)

https://doi.org/10.48550/arXiv.2606.29320 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

analiza aiqbench

Geometric Approach to Zero-Memory Quantum Dot Reservoir Computing

Subiecte conexe