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This study establishes a fundamental classical bound on the information rate of dissipative quantum neurons, demonstrating that while dephasing sharpens activation, it cannot exceed the throughput achievable by a classical two-state jump process. The research provides a hardware-aware benchmark for finite-time quantum neural processing, clarifying the limits of noise-enhanced activation versus actual operational information throughput.
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Classical Bound on the Fisher Information Rate of a Dephasing-Enhanced Quantum Neuron

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arXiv:2609.35076v1 Announce Type: new 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 $\nu$, 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 $\mathcal R\leq\nu(1-s)(1+s)^2/2\leq16\nu/27$, 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.

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 $\nu$, 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 $\mathcal R\leq\nu(1-s)(1+s)^2/2\leq16\nu/27$, 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.

arXiv:2609.35076 [quant-ph]

(or arXiv:2609.35076v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.35076 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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