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Geometric approach to zero-memory quantum dot reservoir computing - engineers memory capacity extrinsically in memoryless systems via spatial degrees of freedom exploiting computational space-time tradeoff.
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| name | zero-memory-quantum-dot-reservoir |
| description | Geometric approach to zero-memory quantum dot reservoir computing - engineers memory capacity extrinsically in memoryless systems via spatial degrees of freedom exploiting computational space-time tradeoff. |
| category | quantum |
| created | 2026-07-06T00:00:00.000Z |
| source | arXiv:2606.29320 |
arXiv:2606.29320 - "Geometric Approach to Zero-Memory Quantum Dot Reservoir Computing" by Bongsu Kim, Oscar Lee, Sangjun Jeon, Kun Woo Kim (2026-06-28)
Physical reservoir computing offers an energy-efficient alternative to conventional neural networks. This paper demonstrates that memory capacity can be engineered extrinsically in memoryless systems by exploiting the computational space-time tradeoff, substituting temporal memory with spatial degrees of freedom.
Spatial Memory Axis: Utilize multidimensional input nodes to function as a spatial memory axis, removing the dependency on intrinsic history-dependent dynamics in the reservoir.
Quantum Dot Nonlinearity: Leverage the discrete energy levels of generalized quantum dots which provide strong nonlinearity crucial for reservoir computing.
Space-Time Tradeoff: Replace temporal memory with spatial degrees of freedom - instead of relying on the reservoir's intrinsic history-dependent dynamics, use spatial configurations to encode temporal information.
Numerical Validation: Validate framework through numerical simulations of generalized quantum dots coupling inherent nonlinearity with extrinsic spatial memory engineering.
zero-memory reservoir computing, quantum dot, space-time tradeoff, spatial memory, physical reservoir, neuromorphic computing, energy-efficient AI, nonlinear computing
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