Classical Bound on the Fisher Information Rate of a Dephasing-Enhanced Quantum Neuron
Abstract
Dephasing can sharpen the activation of a dissipative quantum neuron, but a stationary response does not determine how much input information its output delivers per unit time. For a single qubit with partial-reset updates at rate ν, we derive the threshold response bandwidth and the exact local Fisher information rate of the complete record from repeated projective readouts, including their backaction. Near the activation threshold, the rate obeys ℛ≤ν(1−s)(1+s)2/2≤16ν/27, where s is the retained amplitude of each update. For every fixed finite Markovian dephasing rate, optimization over the readout interval and s gives the same supremum, approached at s=1/3 with arbitrarily rapid ideal readout. A finite measurement dead time instead selects a finite optimal interval, which we obtain by a dimensionless two-parameter optimization. The ideal bound is saturated by a classical two-state jump process: quantum coherence changes finite-cadence performance but cannot raise the optimized bound. These results separate noise-enhanced activation from the operational information throughput of a quantum neuron and provide a hardware-aware benchmark for finite-time quantum neural processing.
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BibTeX
@misc{wang2026classical,
title = {{Classical Bound on the Fisher Information Rate of a Dephasing-Enhanced Quantum Neuron}},
author = {Peng Wang and Yu-Xuan Zhang and Hai-Tao Ding and Leong-Chuan Kwek},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.35076},
eprint = {2609.35076},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.35076},
}
RIS
TY - UNPB
TI - Classical Bound on the Fisher Information Rate of a Dephasing-Enhanced Quantum Neuron
AU - Peng Wang
AU - Yu-Xuan Zhang
AU - Hai-Tao Ding
AU - Leong-Chuan Kwek
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.35076
UR - https://arxiv.org/abs/2609.35076
AB - Dephasing can sharpen the activation of a dissipative quantum neuron, but a stationary response does not determine how much input information its output delivers per unit time. For a single qubit with partial-reset updates at rate ν, we derive the threshold response bandwidth and the exact local Fisher information rate of the complete record from repeated projective readouts, including their backaction. Near the activation threshold, the rate obeys ℛ≤ν(1−s)(1+s)^2/2≤16ν/27, where s is the retained amplitude of each update. For every fixed finite Markovian dephasing rate, optimization over the readout interval and s gives the same supremum, approached at s=1/3 with arbitrarily rapid ideal readout. A finite measurement dead time instead selects a finite optimal interval, which we obtain by a dimensionless two-parameter optimization. The ideal bound is saturated by a classical two-state jump process: quantum coherence changes finite-cadence performance but cannot raise the optimized bound. These results separate noise-enhanced activation from the operational information throughput of a quantum neuron and provide a hardware-aware benchmark for finite-time quantum neural processing.
ER -