# An Artificial Spiking Quantum Neuron

arXiv: https://arxiv.org/abs/1907.06269 · HTML: https://mlsanity.com/paper/1907.06269/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Random Quantum Neural Networks (RQNN) for Noisy Image Recognition](https://mlsanity.com/paper/2203.01764/) (2022)
- [Learning with springs and sticks](https://mlsanity.com/paper/2508.19015/) (2025)
- [Stochastic Neural Networks for Quantum Devices](https://mlsanity.com/paper/2602.22241/) (2026)
- [Quantum inspired qubit qutrit neural networks for real time financial forecasting](https://mlsanity.com/paper/2604.18838/) (2026)
- [QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting](https://mlsanity.com/paper/2605.18333/) (2026)
- [Canonical quantization of neurons](https://mlsanity.com/paper/2607.05000/) (2026)

## Abstract (arXiv)

Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.