Quantum Reservoir Computing for Attention-like Selective Reproduction

@misc{zadehhaghighi2026quantum,
  title = {{Quantum Reservoir Computing for Attention-like Selective Reproduction}},
  author = {Hadi Zadeh-Haghighi and Christoph Simon},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.19272},
  eprint = {2610.19272},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.19272},
}

TY  - UNPB
TI  - Quantum Reservoir Computing for Attention-like Selective Reproduction
AU  - Hadi Zadeh-Haghighi
AU  - Christoph Simon
PY  - 2026
DA  - 2026/10/13
PB  - arXiv
DO  - 10.48550/arxiv.2610.19272
UR  - https://arxiv.org/abs/2610.19272
AB  - Choosing which information to use as the context changes is a basic cognitive operation. We introduce a new task of this kind for quantum reservoir computing (QRC): a binary cue indicates which of two simultaneous input streams the reservoir must reproduce. In simulated spin reservoirs with a trained linear readout, infinitely many measurements (exact expectation values) let the reservoir reproduce the cued stream accurately. At a finite number of measurements N_M, the score exceeds that of a linear readout of the raw inputs only at large N_M because of sampling noise. Performance increases with both entanglement and coherence in the computational basis, and then saturates. These results show that QRC can select and reproduce a cued stream, and they are a starting point for harder cognitive tasks.
ER  -